◈ Homepage — https://scale.com/Products Solutions Research Resources Log In Book Demo The world’s most important decisions need reliable AI systems. Reliable AI has no shortcuts. Scale works across the AI stack: from the data that trains the models you rely on, to the systems that put them to work. APPLICATIONS AI systems that actually work. Most AI deployments in enterprise and government fail. We find the right use case, build the system, and own the outcome. DATA The data powering the world's best AI. The models at the frontier run on Scale data. We source contributors with precision (25% have advanced degrees) and deliver at the bar frontier AI demands. Scroll to explore 90% of the world's leading generative AI model builders are powered by Scale. Artificial Intelligence Real Research Life Science Medicine Energy Infrastructure Sovereignty Robotics Defense Operations Healthcare Autonomy Logistics Get Started CDAO Turning raw, classified data into actionable intelligence. Meta Partnering to accelerate Meta's LLM and Generative AI. Mayo Clinic Reducing physician cognitive load by turning complex patient records into clinical intelligence. Time Powering an interactive AI experience that brings a century of journalism to life. Learn moreabout this Howard Hughes Accelerating real estate development revenue and operations. Physical Intelligence Fuelling the next generation of robotic foundation models with real-world training data. Universal Robots Enabling scalable, real-world Physical AI for industrial robotics. Center for AI Safety Benchmarking the frontier of AI capability with expert-level evaluations. British Petroleum Accelerating enterprise AI adoption across global energy operations. Cengage Enabling smarter, more personalized learning experiences for students and educators at scale. Shore Capital Building agentic AI that drives EBITDA gains across PE portfolio companies. CDAO Turning raw, classified data into actionable intelligence. Meta Partnering to accelerate Meta's LLM and Generative AI. Mayo Clinic Reducing physician cognitive load by turning complex patient records into clinical intelligence. Time Powering an interactive AI experience that brings a century of journalism to life. Learn moreabout this Howard Hughes Accelerating real estate development revenue and operations. Physical Intelligence Fuelling the next generation of robotic foundation models with real-world training data. Universal Robots Enabling scalable, real-world Physical AI for industrial robotics. Center for AI Safety Benchmarking the frontier of AI capability with expert-level evaluations. British Petroleum Accelerating enterprise AI adoption across global energy operations. Cengage Enabling smarter, more personalized learning experiences for students and educators at scale. Shore Capital Building agentic AI that drives EBITDA gains across PE portfolio companies. CDAO Turning raw, classified data into actionable intelligence. Meta Partnering to accelerate Meta's LLM and Generative AI. Mayo Clinic Reducing physician cognitive load by turning complex patient records into clinical intelligence. Time Powering an interactive AI experience that brings a century of journalism to life. Learn moreabout this Howard Hughes Accelerating real estate development revenue and operations. Physical Intelligence Fuelling the next generation of robotic foundation models with real-world training data. Universal Robots Enabling scalable, real-world Physical AI for industrial robotics. Center for AI Safety Benchmarking the frontier of AI capability with expert-level evaluations. British Petroleum Accelerating enterprise AI adoption across global energy operations. Cengage Enabling smarter, more personalized learning experiences for students and educators at scale. Shore Capital Building agentic AI that drives EBITDA gains across PE portfolio companies. CDAO Turning raw, classified data into actionable intelligence. Meta Partnering to accelerate Meta's LLM and Generative AI. Mayo Clinic Reducing physician cognitive load by turning complex patient records into clinical intelligence. Time Powering an interactive AI experience that brings a century of journalism to life. Learn moreabout this Howard Hughes Accelerating real estate development revenue and operations. Physical Intelligence Fuelling the next generation of robotic foundation models with real-world training data. Universal Robots Enabling scalable, real-world Physical AI for industrial robotics. Center for AI Safety Benchmarking the frontier of AI capability with expert-level evaluations. British Petroleum Accelerating enterprise AI adoption across global energy operations. Cengage Enabling smarter, more personalized learning experiences for students and educators at scale. Shore Capital Building agentic AI that drives EBITDA gains across PE portfolio companies. Proven across every industry. 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Get Started PRODUCTS Scale Data Engine Scale GenAI Platform Scale Donovan SOLUTIONS Enterprise Insurance Healthcare US Public Sector Global Public Sector COMPANY About Careers Security Terms Privacy Modern Slavery Statement RESOURCES Blog Contact Us Events Documentation GUIDES Data Labeling ML Model Training Diffusion Models Guide To AI For ECommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions MANAGE YOUR COOKIE PREFERENCES COPYRIGHT © 2026 SCALE AI, INC. ALL RIGHTS RESERVED TERMS OF USE & PRIVACY POLICY This website uses cookies and similar technologies to ensure functionality, evaluate website usage, and to serve marketing content. Visit our Cookie Policy andPrivacy Policy for more information. Cookie Preferences Reject Cookies Accept Cookies ◈ Interior Pages — 35 pages crawledBlog | Scale AI Please rotate your device for the best experience. 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Research SWE Atlas is Complete: Measuring Coding Agents Across the Engineering Loop Government Scale AI Expands Pentagon AI Partnership to $500 Million Product Static AI to Continual Enterprise Learning: A Living Dialect of Tribal Knowledge Company Scale AI Signs MOU with DOE to Advance the Genesis Mission More Posts 06 Scale AI Acquires ICG Solutions to Accelerate National Security AI Apr 20, 2026 07 AI-Native Data Layer: Making Enterprise Data Agent-Ready Product Apr 16, 2026 08 Hidden Risks in Pharma's Most Common GenAI Starting Point Health Apr 13, 2026 09 Introducing Dialect: The Missing Layer Between AI and Enterprise Trust Product Mar 31, 2026 10 Scale AI and BAE Systems Combine Forces to Modernize the Tactical Edge Government Mar 26, 2026 Previous 1 / 13 1 2 3 4 5 ... 13 Next Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy AI Model Leaderboards & Benchmarks | Scale Labs Scale is the AI partner for the US Public Sector | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo US Public Sector Scale powers key computer vision and agentic GenAI programs across the Department of Defense, Intelligence Community, and Federal Civilian agencies. Book a Demo Use Cases Transforming Public Service with AI Scale provides solutions to fine-tune computer vision algorithms and deploy AI agents designed for public sector missions. 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Learn more → RESOURCES Learn More About Our Public Sector Capabilities Customer Blog Machine Perception for Human Protection: Creating Vision Algorithms to Augment Perimeter Security Blog Introducing Thunderforge: AI for American Defense Blog Scale AI & Center for Strategic and International Studies (CSIS) Introduce Foreign Policy Decision Benchmark Blog Introducing the Scale AI and University of Missouri - St. Louis Geospatial Collaborative Blog Scale AI's Proposal for the U.S. AI Action Plan Blog Scale AI products approved for purchase on AWS Marketplace for the U.S. National Security Community Blog Scale Public Sector: Building on Our Progress in 2025 Blog Defense Llama: The LLM Purpose-Built for American National Security Blog Scale AI chosen by the U.S. Army for Robotic Combat Vehicle Program The future of your industry starts here Book a Demo Build AI Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Expanding Our Data Engine for Physical AI | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo ← Blog Product Expanding Our Data Engine for Physical AI By Ben Levin · September 24, 2025 · 4 min read Copy Link Authored by Ben Levin, General Manager, Physical AI at Scale. Data is the foundation of AI progress. At Scale, we spent nearly a decade building the foundation, from pioneering large-scale data operations for autonomous vehicles to processing millions of hours of sensor data for industry leaders. Now, as AI continues to move beyond screens into the physical world, the stakes for high-quality data are even higher. That’s why today, we’re sharing an update on the progress we’ve made delivering our flagship Data Engine to Physical AI and robotics companies. Scale’s Data Engine for Physical AI is a comprehensive data collection and annotation solution that provides the massive, high-quality datasets robotics companies need to train foundation models. What started as our work on autonomous vehicles, is now expanding rapidly to robotics. With more than 100,000 production hours completed at our prototyping laboratory in San Francisco and with the help of contributors globally, Scale is already providing and enriching data for leading Physical AI companies, including Physical Intelligence, Generalist AI, Cobot, and others. “Scale has been a trusted partner in making abundant, high-quality, custom data. We’re excited to be working with them on the future of Physical AI.” – Pete Florence, CEO and co-founder of Generalist AI “At Cobot, our mission is to deploy physical AI at scale through our robot, Proxie. I feel fortunate to have worked with the Scale team in the past and know first-hand that Scale is unique in its ability to quickly adapt to the needs of their partners. We’re excited by the work Scale has already done in building this data set and look forward to partnering to push the data frontier in physical AI.” – Brad Porter, CEO & founder, Cobot The Robotics Data Gap Language models train on trillions of tokens from the internet. Vision models learn from billions of images. However, for robotics, there is no preexisting repository of physical interactions to reference. Unlike text or images, robotic manipulation data can't be scraped from the web. It must be collected, one interaction at a time, in the real world. Today's relevant open-source datasets, including DROID and Open X-Embodiment, offer only about 5,000 hours of interaction data combined – far too little for Physical AI to handle real-world complexity. Scaling laws indicate that true robotics foundation models will need datasets on the scale of those used for language and vision. Until now, progress has been held back by this persistent bottleneck, one that Scale is positioned to solve. We know from experience that collecting a large corpus of data isn't enough – it must be abundant , diverse , and enriched to capture the full complexity of the physical world. Abundant : We've built infrastructure to collect data at scale, both from dedicated data collection robots and from human demonstrations specifically designed to improve robotic capabilities. Scale’s global operations provide the volume and consistency needed for massive datasets. Diverse : Real-world robotics must handle infinite variations in objects, environments, and tasks. We enforce diversity across our data collection to ensure models can generalize beyond narrow scenarios. Enriched : Raw trajectories alone aren’t sufficient. Our datasets are annotated with the same precision we pioneered in computer vision, extending that expertise into robotics. Beyond capturing motion, we layer semantic detail that encodes intent, task structure, and failure modes. We continuously validate annotations by fine-tuning state-of-the-art models with them to confirm they genuinely improve performance. Every piece of data undergoes multiple validation steps, ensuring it's clean, correctly labeled, and valuable for training. Building for Tomorrow's Breakthroughs Scale’s foundational robotics data is designed not just to improve today’s systems, but to unlock the next generation of Physical AI. For researchers and builders, we offer: Custom collection from a wide range of embodiments, working closely with partners to specify tasks, objects, and environments in both lab and field settings to meet real-world needs. Data annotation using machine learning models and heuristics, leveraging the 3D capabilities of the Scale Data Engine. Data streams with a growing library of pre-built datasets. The transformative potential of Physical AI – from industrial to commercial to the home – depends on solving the data challenge. By making high-quality robotics data abundant and accessible, we're accelerating the timeline for reliable AI systems in the physical world. Today represents day one. As Physical AI models grow more capable, data requirements will grow exponentially. We're ready to scale with them. If you're building Physical AI systems and want to learn more about how Scale can accelerate your development, get in touch via [email protected]. Ready to break through your data bottleneck? Scale's team will match your project to the right experts, fast. Talk to our experts Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Agentic Solutions for Healthcare | Scale.com | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Reliable AI for the Future of Healthcare Healthcare enterprises cannot compete without the right technology. Scale transforms your biggest challenges with reliable AI for your most important decisions. Book a Meeting The world's leading enterprises work with Scale. Meet our customers → Why Healthcare Falls Behind These issues aren't new but they continue to drain your staff, systems, and revenue. To remain competitive, you must transform. Inefficient Clinical Workflows Manual, capacity-constrained workflows slow care delivery and bury clinicians in repetitive steps that limit throughput and delay access to care. Data Overload Clinicians are overwhelmed by disconnected information, making it hard to see the full patient picture, slowing decisions and increasing the risk of missed details. Expertise That Doesn’t Scale Critical expertise is trapped in siloed teams and inconsistent workflows, creating long wait times and bottlenecks that drive up costs. How Leading Healthcare Enterprises Use AI to Overcome Their Biggest Challenges. We help healthcare organizations solve the core challenges holding them back. Here's how. Turn unstructured data into actionable clinical intelligence Scale converts documents, voice, imaging, and historical records into clean, traceable insights. Clinicians get faster, more reliable access to the information they need for onboarding, triage, and ongoing care. Train self-improving agents aligned to your clinical workflows Scale builds agents that summarize, extract, and reason over multimodal data with physician-guided feedback. They improve accuracy over time while upholding your clinical standards. Deploy safer, more reliable AI systems across the enterprise Through red teaming and continuous evaluation, Scale ensures every agent operates transparently and safely so care teams can work efficiently and deliver consistent experiences. Fuel Your AI Strategy Learn how we support AI Agents for Healthcare Rad Agent Multi-Agent Radiology Solution The video showcases Rad Agent, a multi-agent radiology solution that proactively manages preparatory clinical tasks before patient appointments. Scalable Infrastructure for Enterprise-Ready AI Agents This video presents Agent Execution, a high-performance foundation designed to manage complex, enterprise-level agent workflows. The future of healthcare starts here Book a Demo Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. 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Generative AI Data Engine | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Generative AI Data Engine Powering the Next Frontier of AI. Book a Demo Trusted by the world's most ambitious AI teams. Meet our customers → PRODUCT OVERVIEW Generative AI Data Engine Enables rapid creation of tailored, high-quality datasets curated by vetted subject matter experts to train the world's most advanced models. Don’t just take our word for it “ Experts are the new GPUs. The largest AI labs are spending huge amounts of money, like huge amounts of money to acquire more valuable tokens, either paying experts to generate it, working through labeling companies like Scale AI or others. ” Nat Friedman Scale's Generative AI Data Engine combines automation and human intelligence to rapidly generate training data tailored to your specific AI goals and data needs. Build AI Improve Your Models By Improving Your Data High-quality training data, curated by subject matter experts, is crucial for developing powerful, accurate, Generative AI models. Key Features Enabling the Next Generation of LLMs Scale has deep experience providing the data underpinning the most advanced LLMs. Ops Center for Quality Control Real-time visibility into data collection and curation Experts, Linguists, and Coders Access a global network of hand-picked experts across diverse fields to build the highest quality datasets Improved Models Train your models with advanced datasets delivered through our purpose-built infrastructure Increased Efficiency Faster, more cost-effective dataset creation Model Evaluation Scale proactively finds and surfaces model weaknesses, including targeted red-teaming. Responsible Development Upholding privacy, fairness, transparency and ethics Demos Scale Data Engine Demos Assisted Instruction Following Demo Span RLHF Human Feedback and Preference Ranking RESOURCES Case Studies & Resources We've worked with the world's leading AI labs for years, and developed more high-quality data than anybody else. Case Studies Customer Case Study: TIME Zeitgeist AI Readiness Report The future of your industry starts here Book a Demo Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Modernizing Tactical Edge with Scale AI and BAE Systems | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo ← Blog Government Scale AI and BAE Systems Combine Forces to Modernize the Tactical Edge · March 26, 2026 · 2 min read Copy Link Today we’re proud to announce a new strategic relationship with BAE Systems Intelligence & Security sector. The collaboration is designed to accelerate the development and fielding of advanced artificial intelligence capabilities into the architecture of the U.S. Department of War’s most capable current and future military platforms and systems. The effort brings together BAE Systems’ decades of mission knowledge in defense operations, systems integration and platforms, with Scale’s proven suite of AI capabilities, including Scale’s Data Engine and Generative AI Platform. It builds on BAE Systems' existing Aided Target Recognition (AiTR) capabilities, which use the Scale Data Engine to train Computer Vision models for engagement options at the tactical edge. To ensure these capabilities are mission-ready, Scale and BAE Systems are utilizing a sim-to-real pipeline to run thousands of iterative, automated simulation runs. By generating synthetic training data, Scale’s software can expose AiTR systems to extreme edge cases - such as obscured sightlines and complex terrain - long before they reach the field. This rigorous simulation environment ensures that when these capabilities are deployed, the software has already trained on more combat scenarios than a human crew could experience in a lifetime. Our strategic approach with BAE Systems, the premier transatlantic defense and aerospace company, ensures that the Department of War has access to the world's most advanced AI tools – enabling the Department to create intelligent, adaptive systems that empower human operators and to out-think and out-pace any adversary. Ready to break through your data bottleneck? Scale's team will match your project to the right experts, fast. Talk to our experts Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. 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Products Solutions Research Resources Log in Book demo Book demo ← Blog General MCIT & Scale AI: Paving the Way for Qatar’s Digital Future By The Scale Team · February 23, 2025 · 2 min read Copy Link The Ministry of Communications and Information Technology (MCIT) and Scale AI, the leader in frontier AI solutions, are announcing a strategic, long-term partnership to drive Qatar’s digital transformation. This partnership will support Qatar’s National Development Strategy and Digital Agenda 2030 . This is a significant milestone in Qatar’s efforts to modernize government operations, enhance public services, and build a future-ready workforce. To start, MCIT and Scale AI will begin a comprehensive exploration of opportunities where AI can streamline processes and deliver significant operational and societal improvements. By using AI-driven approaches—such as predictive modeling, automation, and intelligent data analysis—this work will enhance key sectors outlined in Qatar’s National Development Strategy 3 (NDS3). For example, applying AI-driven approaches will allow for better legal efficiencies through a civil judicial research tool, improved regulatory review processes through automation, and enhanced healthcare administration for better patient experiences. Longer term, the partnership will assess how AI can personalize education, optimize urban planning, strengthen financial systems, and build more sustainability initiatives. As a central component to the partnership, Scale AI will lead an upskilling initiative to empower Qatari government employees, schoolchildren, and college students with essential AI skills. This will be done through targeted training sessions and hands-on workshops, which will allow Qatar to develop robust capabilities without relying on external expertise. Since 2019, Scale has powered the development of nearly every large language model, and will use this knowledge - in partnership with MCIT - to support Qatar’s long-term digital transformation, economic growth, and global leadership in responsible AI adoption. Ready to break through your data bottleneck? Scale's team will match your project to the right experts, fast. Talk to our experts Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy AI Events & Conferences | Scale | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Events Connect, engage, and learn from the world's top experts on AI & Machine Learning. Conference · June 1, 2026 ICRA The IEEE International Conference on Robotics and Automation, the world's premier venue for robotics and automation research and innovation. Learn more Conference · June 2, 2026 Money 20/20 A leading fintech event in Amsterdam where financial services innovators connect to shape the future of money and payments. Learn more Conference · June 8, 2026 Digital Health and AI Innovation Summit A summit convening healthcare and technology leaders to explore how digital health tools and AI are transforming patient care and medical innovation. Learn more Conference · June 14, 2026 DIA Global Annual Meeting 2026 The largest neutral healthcare conference for life sciences professionals, addressing challenges in health, science, policy, and technology. Learn more Conference · June 17, 2026 Retail Innovation and Transformation Assembly An executive assembly focused on retail transformation for the digital age, connecting senior leaders driving innovation in retail. Learn more Conference · June 22, 2026 Bio International The world's largest biotechnology convention, bringing together global biotech leaders for four days of programming, networking, and partnering. Learn more Conference · June 29, 2026 Global AI Show, Riyadh A leading AI conference in Riyadh gathering global experts and industry leaders to advance AI adoption and innovation across sectors. 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Learn more April 23-27, 2026 Conference Scale at ICLR 2026 Scale AI at ICLR 2026, engaging with the machine learning research community and presenting advances in AI training, evaluation, and alignment. Book a Demo April 6, 2026 Conference HumanX Conference An AI-focused conference connecting business leaders and technologists to explore practical applications of artificial intelligence. Learn more March 23, 2026 Conference European Robotics Forum Europe's largest robotics community event, bringing together researchers, industry professionals, and policymakers to advance European robotics. Learn more March 26, 2026 Conference American AI Festival A national festival celebrating AI innovation in the United States, bringing together researchers, entrepreneurs, and policymakers. Learn more March 24, 2026 Conference Economist AI and Business Innovation Summit An Economist-hosted summit exploring how artificial intelligence is reshaping business strategy, operations, and innovation across industries. Learn more Show more events Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyrig Introducing Scale Labs | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo ← Blog Research Introducing Scale Labs By Bing Liu · March 9, 2026 · 4 min read Copy Link Today, we’re announcing the expansion of our research mandate with the launch of Scale Labs. Scale’s Safety, Evaluation, and Alignment Lab (SEAL), launched in 2023, cemented Scale’s authority in benchmarking frontier AI systems, with leading labs citing SEAL benchmarks across major model releases and system cards. Extending SEAL’s work, Scale Labs is our new hub for research initiatives spanning model capability, agentic and multimodal systems, post-training and evaluation methods, enterprise deployment, and our work with global governments and national research institutes to further enable their adoption of AI. Scale is uniquely positioned to expand our research efforts. Through collaborations across research, enterprise, and government, we see how advanced models are built, evaluated, and used in practice. Our history, along with operating the industry’s largest human evaluation infrastructure, gives us rare visibility into how AI systems behave both under rigorous evaluation and in real production environments. As AI advances, so does our perspective. What We Study Scale Labs studies how advanced AI systems actually behave as they become more powerful and deploy in real-world settings. We look at how to test, improve, and evaluate these systems so we understand how they behave in complex workflows, under pressure, and in high-stakes environments. We build better ways of measuring capability, reliability, and risk so that companies, governments, and national research institutes can use these systems with clearer expectations and stronger oversight. Our primary areas of focus include: Evaluation and Measurement: Designing methods to assess reasoning, robustness, calibration, safety and systemic risk as AI capabilities scale. This includes adversarial and adaptive testing approaches that remain informative beyond static benchmarks and better reflect real-world deployment conditions. Agentic and Multimodal Systems: Studying how systems plan, use tools, operate across modalities, and perform long-horizon tasks, including failure modes that emerge beyond static prompt–response settings. Post-Training and Oversight Methods: Advancing reinforcement learning, process supervision, structured feedback techniques, and control protocols to improve reliability, steerability, interpretability, and alignment. Enterprise Deployment: Examining how advanced systems behave in real-world workflows and high-stakes production environments, including reliability under operational constraints. AI Risk and Oversight Infrastructure: Building evaluation and control frameworks that support institutional oversight, including adversarial stress-testing, robustness analysis under distribution shifts, and measurement approaches aligned with national regulatory needs. Together, these areas center on understanding how advanced systems behave outside the lab, helping Scale and our partners to build the infrastructure to measure and oversee them responsibly. Selected Recent Work Recent launches include: SWE Atlas is composed of three separate evaluations with leaderboards that assess how agents understand, validate, and improve real software systems inside real repositories. The evaluations include: Codebase QnA - Understand complex codebases through runtime analysis and multi-file reasoning Test Writing - Write meaningful tests that exercise real functionality to increase code coverage Refactoring - Restructure code to improve readability & maintainability while preserving behavior Long-Horizon Augmented Workflows (LHAW) evaluates underspecification in extended tasks, generating controlled task variants that measure how agents detect ambiguity, seek clarification, and recover performance when information is incomplete. Versioning, Rewards, and Observations (VeRO) provides a reproducible evaluation harness with versioned agent snapshots, budget-controlled evaluation, structured execution traces, and a benchmark suite of target agents and tasks with reference evaluation procedures. Agentic Rubrics generates repository-grounded evaluation rubrics for software bug-fixing tasks, enabling agentic verifiers to score and re-rank candidate patches without executing tests by grading them against structured, context-specific criteria. Previous benchmarks developed under SEAL were designed to remain informative as frontier performance rises. These include Humanity’s Last Exam (HLE), MCP-Atlas, and SWE-Bench Pro, now recommended by OpenAI in place of SWE-Bench Verified. We are also extending benchmarks such as FORTRESS for use in national and public-sector contexts, expanding their role in AI safety evaluation. Looking Ahead The next phase of AI will be defined by how AI systems behave in the real world, in real-world workflows, institutions, and decision-making processes. Scale Labs exists to help the field and national governments understand and shape that behavior. Our work is already appearing across new research releases and academic venues, including recent papers accepted to ICLR exploring rubric-based rewards, evaluation-driven reinforcement learning, and agentic safety measurement. Explore our research page and ongoing commentary on our blog and on X . Ready to break through your data bottleneck? Scale's team will match your project to the right experts, fast. Talk to our experts Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. 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We have an exciting roadmap ahead that we will be announcing shortly, and we're going to continue to be partnering with Scale AI, and I'm really excited about that. ” “ We wanted to not just stand up a demo or POC, but deploy production-ready use cases and infrastructure. With Scale GenAI Platform, we were able to quickly launch our first use case: a GenAI solution that makes it easy for users across Global Atlantic to get information out of our Enterprise Data Hub. This is enabling data-driven decision making and shortening the time to insights from days or weeks down to seconds. ” “ Scale AI is really the backbone of our AI success. It's critical and I'm excited to keep building forward that relationship and delivering more for Howard Hughes." - Carlos Olea, CFO, Howard Hughes. ” Jessica Sibley Padma Elmgart Carlos Olea The race is on. Everyone is using AI. But only Scale makes it uniquely yours. Learn More Most AI gets smarter for everyone. Dialect learns from your experts every day, turning their judgment into a compounding advantage that grows more powerful, and more uniquely yours, over time. Three Pillars. One Unfair Advantage. Integrated experts We embed directly in your organization to help you identify the right use cases and build them correctly from your office, not ours. Technology Our platform and internal tooling are built to move fast, ready to stand up in your environment within a day of a demo. Applied AI Research Our frontier research team isn't just following the technology, they're building and evaluating it alongside the labs that make it. Powered by the Scale Generative AI Platform Agentic solutions are built and deployed on Scale’s platform, enabling the full lifecycle of AI systems – from development to real-world operation. Learn More Build & Improve AI Systems Create, evaluate, and refine AI systems with the data, feedback, and workflows needed to make them reliable. 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Products Solutions Research Resources Log in Book demo Book demo All Guides ← Guide 06 Guide to Large Language Models Large language models (LLMs) are transforming how we create, understand our world, and how we work. We created this guide to help you understand what LLMs are and how you can use these models to unlock the power of your data and accelerate your business. April 9, 2026 25 min read Contents What are Large Language Models? Why are Large Language Models important? Common Use Cases for Large Language Models A Brief History of Language Models Model Size and Performance Fine-Tuning Large Language Models Reinforcement Learning from Human Feedback (RLHF) LLM Prompt Engineering Conclusion Share What are Large Language Models? Large language models (LLMs) are machine learning models trained on massive amounts of text data that can classify, summarize, and generate text . LLMs such as OpenAI’s GPT-4, Google’s PaLM 2, Cohere’s Command model, and Anthropic’s Claude, have demonstrated the ability to generate human-like text, often with impressive coherence and fluency . Until the arrival of ChatGPT, the most well-known examples of large language models were GPT-3 and BERT, which have been trained on vast amounts of text data from the internet and other sources. Generally, LLMs are capable of a wide variety of natural language processing (NLP) applications, including copywriting, content summarization, code generation and debugging, chatbots, question answering, and translation . At a high level, large language models are language prediction models . These models aim to predict the most likely next word given the words provided as input to the model, also called prompts . These models generate text one word at a time based on a statistical analysis of all the “ tokens ” they have ingested during training (tokens are strings of characters that are combined to form words). LLMs have a wide array of capabilities and applications that we will explore in this guide. Despite the significant progress in making these models more capable and widely accessible , many organizations are still uncertain about how to adopt them properly. From the Scale Zeitgeist 2023 report , we found that while most respondents (60%) are experimenting with generative models or plan on working with them in the next year, only 21% have these models in production. Many organizations cited a lack of the software and tools, expertise, and changing company culture as key challenges to adoption. We wrote this guide to help you get a better understanding of large language models and how you can start adopting them for your use cases . Why are Large Language Models important? Large language models have revolutionized natural language processing and have a wide range of applications . These models are transforming how we create, understand our world, and conduct business. Large language models help us write content like blogs, emails, or ad copy more quickly and creatively. They enable developers to write code more efficiently and help them find bugs in large code bases . Developers can also integrate their applications with LLMs using English-language prompts without needing a machine learning background, accelerating innovation. Large language models summarize long-form content so that we can quickly understand the most critical information from reports, news articles, and company knowledge bases. Chatbots are finally living up to their promise of enabling businesses to streamline operations while improving customer service . Large Language Models are more available to a wider audience than ever, as companies such as OpenAI, Anthropic, Google, Stability AI, and Cohere, provide APIs or open-source models to be used by the larger community. Additionally, the talent pool of machine learning engineers is growing and new roles such as "prompt engineer" are becoming popular ( source ). Due to the large amount of data they have been trained on , large language models generalize to a wide range of tasks and styles . These models can be given an example of a problem and are then able to solve problems of a similar type. However, out of the box, these models are poor specialists. To take full advantage of LLMs, businesses need to fine-tune models on their proprietary data . For example, consider a financial services company looking to perform investment research. As base models only have access to outdated publicly available data, they will provide generic information about stocks or other assets but often will be unable or will flat-out refuse to provide investment advice. Alternatively, a fine-tuned model with access to private research reports and databases is able to provide unique investment insights that can lead to higher productivity and investment returns. When used properly, LLMs help organizations to empower their employees, increase their efficiency , and are the foundation for better customer experience . We will now explore how these models work and how to deploy them properly to maximize the benefits for your business. Common Use Cases for Large Language Models LLMs are capable of a wide variety of tasks, the most common of which we will outline here. We will also discuss some domain-specific tasks for a select few industries. Classification and Content Moderation Large language models can perform a wide range of natural language processing tasks, including classification tasks. Classification is the process of assigning a given input to one or multiple predefined categories or classes . For example, a model might be trained to classify a sentence as either positive or negative in sentiment. Beyond sentiment analysis , LLMs can be u sed to detect the reasons for customers' calls (no more needing to sit through long phone menus to get to the right agent) or properly organize user feedback between UX suggestions, bug reports, or feature requests. Content moderation is also a common application of LLMs classification power. A common use case is flagging if users are posting toxic and inappropriate content . LLM can be fine-tuned to quickly adapt to new policies, making them highly versatile tools for content moderation. Classification: Classify each statement as "Bearish", "Bullish", or "Neutral" Text Generation One of the most impressive capabilities of large language models is their ability to generate human-like text . Large language models can produce coherent and well-written prose on almost any topic in an instant . This ability makes them a valuable tool for a variety of applications, such as automatically generating responses to customer inquiries or even creating original content for social media posts . Users can request that the response is written in a specific tone , from humorous to professional, and can mimic the writing styles of authors such as William Shakespeare or Dale Carnegie. Text Extraction Large language models can also extract key information from unstructured text . This can be particularly helpful for search applications or more real-time use cases like call center optimization , such as automatically parsing a customer's name and address without a structured input. LLMs are particularly adept at text extraction because they importantly understand the context of words and phrases and can filter extraneous information from important details . Summarization Large language models can also perform text summarization, which is the process of creating a concise summary of a given piece of text that retains its key information and ideas . This can be useful for analyzing financial statements, historical market data, and other proprietary data sources and providing a summary of the documents for financial analysts. Text extraction combined with text summarization is a very powerful combination. For companies or industries w Scale Data Engine | AI Training Data at Scale | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Data Engine Collect, Curate, and annotate data. Train models and evaluate. Repeat. Book a Demo The Best In The Business The Scale Data Engine is trusted by the world's leading ML teams to accelerate the development of their models. Quality Scale can provide the core tenet of any dataset with high-quality labels from domain experts. Cost Effective Easily find, categorize, and fix model failures with Scale's Data Engine. Then, optimize labeling spend with high-value curated data. Scalability Scale's data engine can support any ML project from lower-volume experiments to high-volume production projects. Diversity Scale delivers the greatest variety and diversity of data to help deliver the greatest value to your model performance. Customer Case Study TIME and Scale partnered to transform media publishing workflows with generative AI experiences built for a global audience. TIME Read customer story Build AI Powering Frontier AI Next Generation AI powered by world-class data. Generative AI Powering the next generation of Generative AI Scale Generative AI Data Engine powers many of the most advanced LLMs and generative models in the world through world-class RLHF, data generation, model evaluation, safety, and alignment. Book a Demo Build AI AI Text Generator WHAT IS THE DATA ENGINE The One-Stop-Shop For Building AI Data engine is the process of improving machine learning models with high quality, diverse and large datasets powered by experts. Unlock model performance with the Scale Data Engine. Generative AI Data Engine Generation After initial pre-training, create complex prompt-response pairs from scratch. RLHF Apply human preferences to model outputs. Red Teaming Use prompt injection techniques to find vulnerabilities. Evaluation Evaluate your model against a set of complex and diverse prompts to find weak points. DATA INPUTS Supported Annotation Types Scale Text Document Processing Natural Language Processing Transcription Content & Language Scale Image Electro Optical Infrared Transcription Scale Video Full Motion Video Natural Language Processing Scale 3D Sensor Fusion LiDAR RESOURCES Learn More About The Data Engine Blog Why Is ChatGPT So Good? Guide Guide to Data Annotation Guide Guide: Computer Vision Guide Guide: Training & Building Models Blog Why Is ChatGPT So Good? Guide Guide to Data Annotation Guide Guide: Computer Vision Guide Guide: Training & Building Models Don't just take our word for it “ Scale has made it easier for us to gather annotations at a good price point. The UI is simple to navigate, and the built in worker evaluation pipeline and batch options saves us time and helps enforce best practices so that we can get high-quality training data. ” Cassandra Ung The future of your industry starts here Book a Demo Build AI Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Donovan: Empowering the Public Sector with AI Agents | Scale AI | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Donovan Deploy specialized AI Agents for mission-critical workflows. Book a Demo Product Overview Customize, Evaluate, and Deploy AI Agents Deploy mission-tailored AI agents from concept to combat in record time with Donovan. Put advanced AI capabilities in the hands of warfighters when and where they need it most. J1 Agent / Agent Factory Draft Deploy Assistant Tools Settings System Prompt You are a military intelligence analyst. Analyze documents accurately and always cite sources. Knowledge Base GDELT Event Data DoD Policy Documents Theatre Operations DB Llama 3.3 70B Instruct Active Pipeline GDELT Connector Source Knowledge Base Retrieval Reranker Filter Llama 3.3 70B LLM Output Preview Based on GDELT event data from Q4 2024, the situation in the specified region shows escalating activity with 3 key flash points identified... Agent Factory Use Donovan’s integration with SGP to build and customize AI agents with a no-code interface by accessing data sources, creating knowledge bases, leveraging state-of-the-art models, assigning agent instructions, and connecting agents to existing systems and tools. Models / Compare Models Model 1 Scale Gemma 7B Instruct Model 2 Llama 3.1 70B Instruct Compare Metrics Compare Responses Metric Gemma 7B Winner Llama 70B Factual accuracy 0.81 0.91 Hallucination rate 0.24 0.14 Context recall 0.71 0.79 Latency p95 1.4s 2.1s Test & Evaluate Compare model responses and performance metrics from leading models to determine which model is best suited for your mission and workflow - backed by rigorous testing frameworks that prioritize reliability, security, and alignment with DoD AI readiness goals. Agent Arsenal Discover AI Agents optimized for mission-critical workflows Search for agents and applications... All Intelligence Planning Analysis Reports GDELT Agent Multi-tool analysis of global events and tabular intelligence data Exhaustive Search Discover all relevant documents across knowledge bases Artillery Capabilities Analyze capabilities and produce mission announcements Flashpoint Chat Real-time intelligence analysis and threat monitoring Mission Analyst Analyze mission data and craft detailed operational reports Address Extraction Extract location data from unstructured intelligence text Agent Arsenal Discover and operationalize AI agents and applications designed to force-multiply your organization - aligned with DoD AI ethics principles and engineered for accountability, speed, and scale. Why Scale Foundation of AI Agents Discover the core building blocks that power effective mission-ready AI agents. Donovan Applications Donovan Assistants Donovan Agents Models Building Blocks Evaluations Data Flywheel Monitoring & Insights Guardrails & Redteaming Ingest Data Agentic RAG Agent Arsenal Agent Interaction Report Generation Access fresh intelligence with Donovan’s prebuilt data connectors. Securely interact with your documents via local upload, cloud, or API. Leverage data connectors to external databases like GDELT. Why Scale Achieve AI Overmatch Our adversaries aren’t waiting to field AI systems. Ensure decisive advantage with the most advanced, mission-ready AI capabilities the U.S. has in its toolkit. AI Expertise Scale works with the leading commercial foundation model providers and bring lessons learned to our public sector engagements. Model Performance One of Scale’s core competencies is model fine-tuning. Scale can customize model specific to your use-case or leverage pre-existing models like Defense Llama. Time to Value Access no-code tools to customize AI agents on your own without reliance on external developers. Flexibility Select your preferred method for agent and model deployment and hosting. Seamlessly embed Donovan with your existing applications and tools. Traceability Transparent detailed reasoning helps users understand the steps and resources AI agents used to arrive at an answer. Model Agnostic Leverage your preferred cloud and model provider with Donovan. Select models that account for mission parameters like cost, speed, and expert capabilities. Supported Environments Trusted & Secure Donovan is available on classified and air gapped networks. Unclassified/CUI GovCloud DISA IL4 and FedRAMP High Authorized Classified Networks Field-ready on secure government networks Cloud Agnostic Kubernetes containerized platform The future of your industry starts here Book a Demo Try Donovan Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Diffusion Models: A Practical Guide | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo All Guides ← Guide 02 Diffusion Models: A Practical Guide Diffusion models have the power to generate any image that you can imagine. This is the guide you need to ensure you can use them to your advantage whether you are a creative artist, software developer, or business executive. April 9, 2026 35 min read Contents Introduction What are Diffusion Models? Diffusion Models: Why Are They Important? Getting Started with Diffusion Models Diffusion Model Prompt Engineering Diffusion Model Limitations Diffusion Models: Additional Capabilities and Tooling Diffusion Models: Practical Applications for today and tomorrow Conclusion Share Introduction With the Release of Dall-E 2 , Google’s Imagen , Stable Diffusion , and Midjourney , diffusion models have taken the world by storm, inspiring creativity and pushing the boundaries of machine learning. These models can generate a near-infinite variety of images from text prompts, including the photo-realistic, the fantastical, the futuristic, and of course the adorable. These capabilities redefine what it means for humanity to interact with silicon, giving us superpowers to generate almost any image that we can imagine. Even with their advanced capabilities, diffusion models do have limitations which we will cover later in the guide. But as these models are continuously improved or the next generative paradigm takes over, they will enable humanity to create images, videos, and other immersive experiences with simply a thought. In this guide, we explore diffusion models, how they work, their practical applications, and what the future may have in store. What are Diffusion Models? Generative models are a class of machine learning models that can generate new data based on training data. Other generative models include Generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Flow-based models. Each can produce high-quality images, but they all have limitations that make them inferior to diffusion models. At a high level, Diffusion models work by destroying training data by adding noise and then learn to recover the data by reversing this noising process. In Other words, Diffusion models can generate coherent images from noise. Diffusion models train by adding noise to images, which the model then learns how to remove. The model then applies this denoising process to random seeds to generate realistic images. Combined with text-to-image guidance, these models can be used to create a near-infinite variety of images from text alone by conditioning the image generation process. Inputs from embeddings like CLIP can guide the seeds to provide powerful text-to-image capabilities. Diffusion models can complete various tasks, including image generation, image denoising, inpainting, outpainting, and bit diffusion. Popular diffusion models include Open AI’s Dall-E 2, Google’s Imagen, and Stability AI's Stable Diffusion. Dall-E 2 : Dall-E 2 revealed in April 2022, generated even more realistic images at higher resolutions than the original Dall-E. As of September 28, 2022 Dall-E 2 is open to the public on the OpenAI website, with a limited number of free images and additional images available for purchase. Imagen is Google’s May 2022, version of a text-to-image diffusion model, which is not available to the public. Stable Diffusion : In August 2022, Stability AI released Stable Diffusion, an open-source Diffusion model similar to Dall-E 2 and Imagen. Stability AI’s released open source code and model weights, opening up the models to the entire AI community. Stable Diffusion was trained on an open dataset, using the 2 billion English label subset of the CLIP-filtered image-text pairs open dataset LAION 5b , a general crawl of the internet created by the German charity LAION. Midjourney is another diffusion model released in July 2022 and available via API and a discord bot. Simply put, Diffusion models are generative tools that enable users to create almost any image they can imagine. Diffusion Models: Why Are They Important? Diffusion models represent that zenith of generative capabilities today. However, these models stand on the shoulders of giants, owing their success to over a decade of advancements in machine learning techniques, the widespread availability of massive amounts of image data, and improved hardware. For some context, below is a brief outline of significant machine learning developments. In 2009 at CVPR, the seminal Imagenet paper and dataset were released, which contained over 14 million hand-annotated images. This dataset was massive at the time, and it remains relevant to researchers and businesses building models today. In 2014, GANs were introduced by Ian Goodfellow, establishing powerful generative capabilities for machine learning models. In 2018 LLM’s hit the scene with the original GPT release, followed shortly by its successors GPT-2, and the current GPT-3, which have text generation capabilities. In 2020, NeRFs allowed the world to produce 3D objects from a series of images, and known camera poses. Over the past few years, Diffusion models have continued this evolution, giving us even more powerful generative capabilities. What about diffusion models makes them so strikingly different from their predecessors? The most apparent answer is their ability to generate highly realistic imagery and match the distribution of real images better than GANs . Also, diffusion models are more stable than GANs, which are subject to mode collapse , where they only represent a few modes of the true distribution of data after training. This mode collapse means that in the extreme case, only a single image would be returned for any prompt, though the issue is not quite as extreme in practice. Diffusion models avoid the problem as the diffusion process smooths out the distribution, resulting in diffusion models having more diversity in imagery than GANs. Diffusion models also can be conditioned on a wide variety of inputs, such as text for text-to-image generation, bounding boxes for layout-to-image generation, masked images for inpainting, and lower-resolution images for super-resolution. The applications for diffusion models are vast, and the practical uses of these models are still evolving. These models will greatly impact Retail and eCommerce, Entertainment, Social Media, AR/VR, Marketing, and more. Getting Started with Diffusion Models Web applications such Open AI’s Dall-E 2 and Stable Diffusion’s DreamStudio make diffusion models readily available. These tools provide a quick and easy way for beginners to start with diffusion models, allowing you to generate images with prompts and perform inpainting and outpainting. DreamStudio offers more control over the output parameters, while Dall-E 2’s interface is simpler with fewer frills. Each platform provides free credits to new users, but will charge a usage fee once those credits are depleted. DreamStudio : DreamStudio from Stability AI is a quick way for users to experience Stable Diffusion without worrying about the infrastructure details. There is tooling for image generation, inpainting, and outpainting. Uniquely, the interface enables users to specify a random seed, providing the ability to traverse the latent space while holding a prompt fixed (more to come on this later). New users get 200 free credits. Dall-E 2: OpenAI recently announced that Dall-E 2 is now generally available to all users, coming out of its previously closed beta. Dall-E 2 provides a simple user interface without many frills, to generate images, inpainting, and outpainting. Local Installation: Stability AI broke headlines when it announced that it was open-sourcing both the model weights and source code for its Diffusion model Stable Diffusion. You can download and install Stable Diffusion on your loc Customer Success Story: TIME | Scale AI | Scale AI | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Disrupting the media industry with TIME AI. Overview TIME is Redefining the Media and Publishing Industry with Generative AI TIME and Scale revolutionize media publishing by leveraging the power of Generative AI to bring TIME’s trusted journalism to the world in exciting new ways. Together we built TIME AI, the first interactive Generative AI experience for TIME Person of the Year. The TIME AI experience includes text and audio summaries, voice interactions, translations, audio, and conversational chat, enabling readers to immerse themselves in the history and background of the Person of the Year. Custom guardrails keep the model on topic and safe for use. Watch TIME CEO Jess Sibley and TIME COO Mark Howard discuss how Scale delivered this impactful and trustworthy GenAI experience in just two months. “ We have a lot more to do. We have an exciting roadmap ahead that we will be announcing shortly, and we're going to continue to be partnering with Scale AI, and I'm really excited about that. — Jessica Sibley — Chief Executive Officer — TIME ” The Problem TIME Needed to Quickly Build an Immersive and Trustworthy AI Solution The media industry faces an existential risk due to the rise of Generative AI technology. As media consumption preferences are changing, TIME wanted to lean in and push the boundaries of what it meant to engage with their content. They needed to move past a traditional article's static, linear experience and even beyond an out-of-the-box conversational chat experience. They needed a solution that was unique to TIME and that reflected the organization's high quality standards and their foundation of trusted journalism. TIME wanted to build an AI solution that could guide readers deeper into articles, enhance comprehension, maintain their editorial voice, and operate safely—all while blocking inappropriate content. TIME also needed this solution to be safe to maintain their foundation of trusted journalism. TIME person of the Year is usually highly contentious and the subject of lively debate. Finally, TIME needed a partner that was capable of accelerating from proposal to production solution in just two months. “ We knew and anticipated that there would be half of the population who would be very excited about it and there would be half who would have issue with it. And because of that, we wanted to make sure that all of the guardrails were in place, making sure that this experience was the most reliable, trusted, safe environment for our content and our journalism to still be the primary experience, but to give users that interactivity that they've come to expect. — Mark Howard — Chief Operating Officer — TIME ” The Solution Scale Delivers a Unique and Trustworthy AI Solution in Two Months Scale designed and built a dynamic, interactive, and multimodal solution to highlight the 2024 TIME Person of the Year Donald Trump, and the winners from the three previous years, Taylor Swift, Volodymyr Zelensky, and Elon Musk. Text and audio summaries make it easier to consume the articles, while voice input and conversational chat add depth to the experience. The articles were translated into seven languages: English, Spanish, French, German, Chinese, Russian, and Ukrainian. The conversational chat experience is contextually aware of the Person of the Year articles and other related articles from TIME’s archives. Readers can ask TIME AI questions like: "What are Donald Trump’s plans for foreign policy?" or "How do Taylor Swift’s record sales compare to other successful musicians in the 20th and 21st century?" or "Has the Russia - Ukraine border been disputed in the past?". They can also catch up on past editions by listening to articles in a podcast format while multitasking or receive concise summaries focused on the subjects within the content that matter most to them. Implementation Scale integrated fine-tuned models from its partner OpenAI to provide the chat, summarization, and translation capabilities, and ElevenLabs for audio functionality. Scale fine-tuned these models on TIME’s vast archives of proprietary articles. Scale implemented comprehensive guardrails for the system by drawing on extensive previous red-teaming and AI safety work done for the U.S. Government and Fortune 500 enterprises. To tune the TIME AI guardrails, Scale used in-house red-teaming to establish and enforce strict safety standards. Over 7,000 attack vectors were tested to protect against AI misuse and custom-tailored attacks. Red-teaming is essential in the AI development lifecycle, serving as a safeguard to proactively identify vulnerabilities, biases, and harmful behaviors that could arise under unexpected conditions. By constantly iterating and refining the model's responses through these techniques, we ensure that the model is resilient against a wide spectrum of adversarial inputs and tailored to TIME's editorial voice. Scale delivered these features in less than two months from proposal to production deployment. The Scale team accelerated the development of this application with Scale GenAI Platform (SGP), using building blocks for fine-tuning, frontier evaluations, red-teaming, guardrails, monitoring, and more. “ All the guardrails needed to be in place so that this experience was the most reliable, trusted, and safe environment for our content and our journalism. Scale brought a depth of expertise and information to the table to guide us through that process. — Mark Howard — Chief Operating Officer — TIME ” The Result TIME Redefines How People Experience and Engage with their Trusted Journalism With TIME AI, TIME has extended its audience, expanded accessibility, and increased engagement. But this project was about more than just driving engagement—it was about staying relevant in a changing media landscape. By providing dynamic, interactive, multimodal experiences that are personalizable and customizable, TIME positioned itself as a disruptive leader. The innovative partnership between TIME and Scale garnered attention not just for the Person of the Year content, but for the groundbreaking way it was delivered. This strengthened TIME's position as a forward-thinking, relevant media brand at the intersection of trusted journalism and cutting-edge technology. Looking ahead, TIME and Scale are building even more ambitious and disruptive experiences across their digital footprint. “ We got so much recognition in the technology, media, and advertising industries, for being innovative leaders in this space, and for producing something really exciting, engaging, effective, also safe. We have a lot more to do. We have an exciting roadmap ahead that we will be announcing shortly, and we're going to continue to be partnering with Scale AI, and I'm really excited about that. — Jessica Sibley — Chief Executive Officer — TIME ” The future of your industry starts here Book a Demo → Build AI → Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Scale Privacy Policy | Scale AI | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Legal Scale Acceptable Use Policy Scale Cookie Policy Scale End User Terms of Use Scale Event Terms & Conditions and Guidelines Form 8937 Personnel, Applicant, and Candidate Privacy Policy Scale Main Services Agreement Scale Nucleus Open Source Licenses Scale Privacy Policy Scale Product Terms Scale Rapid Open Source Licenses Scale Subprocessors Scale Website Terms and Conditions Scale’s Business Partners Code of Conduct Scale AI Scale Privacy Policy Last Updated: July 1, 2024 (view prior version here ) Scale respects your privacy and is committed to protecting your personal information. Your privacy is important to us. Please read this policy carefully to understand how we will collect, use, and disclose your information, and what choices you have with respect to your information. You can also access a printable version of this Privacy Policy here . 1. 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We may disclose your information to third parties if we determine such disclosure is reasonably necessary to: (a) comply with applicable law, regulation, legal process, or government request, (b) protect any person from death or serious bodily injury, (c) prevent fraud, abuse, or security issue of Scale or other users, and (d) to protect Scale’s or its licensors’ rights, property, safety, or interest. Consent. We may also disclose or share your personal information where you h The Next Phase of U.S. AI Policy: Governance and Leadership | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo ← Blog Government The Next Phase of U.S. AI Policy: Governance, Implementation, and Global Leadership By Max Fenkell · January 13, 2026 · 7 min read Copy Link Artificial intelligence is no longer a subject of theoretical policy debate; it is now a test of governance. Since the launch of ChatGPT in November 2022, the United States has moved rapidly from studying AI’s capabilities and risks to enabling its growth. During this period, early restraint helped avoid premature regulation and preserve U.S. leadership. Over the past year, Congress and the Trump Administration have begun using the full range of policy tools available to them—accelerating data center permitting, directing investment through the Departments of Energy and War, and enabling access to the American AI technology stack abroad. These actions mark a clear shift from experimentation to execution. But they do not yet amount to a governing strategy capable of sustaining U.S. leadership in AI. The central question facing policymakers is no longer whether AI will reshape the economy and national security. It is whether the federal government can govern, deploy, operationalize, and scale AI systems to maintain leadership. If 2023 and 2024 were about understanding AI, and 2025 was about enabling growth, then 2026 must be the year of governance and implementation. Securing U.S. leadership in the AI era will require progress on three priorities. AI Governance Should Modernize Existing Law, Not Replace It Challenge: Effective AI governance must be grounded in how AI systems are used, not in the technology itself. Use-based regulation is the only approach that preserves innovation while placing guardrails where real risks arise. Treating all AI systems as interchangeable regardless of context, function, or impact creates regulatory uncertainty without improving reliability, accountability, or outcomes. In practice, AI is deployed in two fundamentally different ways: the broad release of general-purpose models, and the use-case-specific application of AI systems within defined operational and regulatory environments. Both are essential to the AI ecosystem, but they present materially different risk profiles and therefore require different forms of oversight. For use-case-specific AI applications, the appropriate starting point is the existing regulatory system. U.S. law already governs outcomes and conduct across sectors such as financial services, housing, healthcare, and consumer protection. In many cases, the law itself does not need to change. What is missing is clear, authoritative guidance on how existing requirements apply, and be complied with, when AI systems are involved. That guidance should modernize compliance rather than invent new regulatory regimes. In practice, this may mean replacing static practices, such as generalized employee training or paper-based controls, with obligations to work with third parties to test, evaluate, and red-team AI systems against real-world risks. The underlying legal standards remain the same; what changes is how you demonstrate compliance in an AI-enabled environment. In parallel, governance of the broad deployment of general-purpose models must be clearly scoped and disciplined. Overly expansive, technology-based regulation at the model level risks slowing adoption and investment without addressing downstream harms, which overwhelmingly arise at the point of use. Recommendation: Congress and the Administration should direct federal agencies to adopt a use-based approach to AI governance and issue clear, authoritative guidance clarifying how existing laws apply to AI-enabled systems and how companies can comply with them via third party testing. Agencies should distinguish between areas where current frameworks are sufficient, where guidance is needed, and where regulatory gaps exist. U.S. AI Leadership Depends on Government-Wide Implementation Challenge: Much of the federal AI agenda has focused on governance frameworks and access to technology. That work is necessary but insufficient. Winning on AI requires adoption, integration, and sustained operationalizing across government departments and agencies, and the federal government is not yet positioned to do so at scale. Recent efforts to make large language models available to government users are a positive first step. These tools can improve back-office productivity and day-to-day efficiency. However, access alone will not resolve the government’s most persistent operational bottlenecks. The hardest problems—permitting delays, acquisition backlogs, compliance workflows, and benefits administration—require use-case-specific AI systems designed to operate within complex, regulated processes. Permitting reform illustrates the challenge. Despite wide recognition of the need to modernize the permitting process, progress has been incremental. Historically, agencies faced a trade-off between speed and completeness. Today, that is no longer the case: meaningful improvement will require AI applications that support the entire permitting workflow from intake and review to interagency coordination and decision-making. Even then, deployment remains difficult. Legacy policies, fragmented data systems, and unclear operational ownership continue to block implementation across agencies. Data remains siloed, infrastructure uneven, and responsibility for AI deployment diffused. As a result, promising pilots stall and successful proofs of concept fail to scale. True AI implementation in government will require a different foundation: routine data sharing across agencies, government-wide AI-ready data and infrastructure, and clear accountability for outcomes. Today, those foundations do not exist. Recommendation: Congress should pass legislation or the Administration should take action to establish a Chief AI Officer interagency working group, led by the White House, with responsibility for removing—not merely identifying—barriers to AI implementation across the federal government. The group should drive actions on data sharing, AI-ready infrastructure, and operational ownership for priority use cases, with defined deliverables and timelines. Advancing U.S. Leadership through AI Exports and Standards Challenge : We are entering a decisive period. Over the next year, foundational standards governing how AI systems are built, evaluated, and deployed worldwide will take shape. If the United States does not lead that process, others will. China has already begun to export not only AI technologies, but also the standards and governance models that accompany them. The strategic consequences of losing that contest would extend far beyond the technology sector. This risk is not theoretical. In the race to shape global 5G standards, China moved quickly, aligned industrial policy with standards bodies, and embedded its technology across global markets. The United States responded too slowly and without sufficient coordination—and continues to bear the consequences. The United States must establish itself as the global standard-setter for artificial intelligence. Domestic adoption and sound governance are necessary, but insufficient. Global leadership will be determined by which country’s technology, standards, and operating norms are adopted by allies and partners at scale. The Trump Administration’s Promoting the Export of the American AI Technology Stack Executive Order reflects a necessary shift in approach. It recognizes that we must assert leadership, not assume it is ours for the taking. To secure durable leadership, we must go beyond exporting the technology stack: U.S. technical standards must become the global default. The Department of Commerce’s Center for AI Standards and Innovation (CAISI) is central to this effort. Its ability Data Labeling: The Authoritative Guide | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo All Guides ← Guide 01 Data Labeling: The Authoritative Guide The success of your ML models is dependent on data and label quality. This is the guide you need to ensure you get the highest quality labels possible. April 9, 2026 73 min read Contents Data Labeling for Machine Learning What is Data Labeling? Why is Data Annotation Important? How to Annotate Data High-Quality Data Annotations Data Labeling for Computer Vision NLP Data Labeling Conclusion Additional Resources Share Data Labeling for Machine Learning Machine learning has revolutionized our approach to solving problems in computer vision and natural language processing. Powered by enormous amounts of data, machine learning algorithms are incredibly good at learning and detecting patterns in data and making useful predictions, all without being explicitly programmed to do so. Trained on large amounts of image data, computer models can predict objects with very high accuracy. They can recognize faces, cars, and fruit, all without requiring a human to write software programs explicitly dictating how to identify them. Similarly, natural language processing models power modern voice assistants and chatbots we interact with daily. Trained on enormous amounts of audio and text data, these models can recognize speech, understand the context of written content, and translate between different languages. Instead of engineers attempting to hand-code these capabilities into software, machine learning engineers program these models with a large amount of relevant, clean data. Data needs to be labeled to help models make these valuable predictions. Data labeling is one of machine learning's most critical and overlooked activities. This guide aims to provide a comprehensive reference for data labeling and to share practical best practices derived from Scale's extensive experience in addressing the most significant problems in data labeling. What is Data Labeling? Data labeling is the activity of assigning context or meaning to data so that machine learning algorithms can learn from the labels to achieve the desired result. To better understand data labeling, we will first review the types of machine learning and the different types of data to be labeled. Machine learning has three broad categories: supervised, unsupervised, and reinforcement learning. We will go into more detail about each type of machine learning in Why is Data Annotation Important ? Supervised machine learning algorithms leverage large amounts of labeled data to “train” neural networks or models to recognize patterns in the data that are useful for a given application. Data labelers define ground truth annotations to data, and machine learning engineers feed that data into a machine learning algorithm. For example, data labelers will label all cars in a given scene for an autonomous vehicle object recognition model. The machine learning model will then learn to identify patterns across the labeled dataset. These models then make predictions on never before seen data. Types of Data Structured vs. Unstructured Data Structured data is highly organized, such as information in a relational database (RDBMS) or spreadsheet. Customer information, phone numbers, social security numbers, revenue, serial numbers, and product descriptions are structured data. Unstructured data is data that is not structured via predefined schemas and includes things like images, videos, LiDAR, Radar, some text data, and audio data. Images Camera sensors output data initially in raw format and then converted to .png or preferably .jpg files, which are compressed and take up less storage than .png, which is a serious consideration when dealing with the large amounts of data needed to train machine learning models. Image data is also scraped from the internet or collected by 3rd party services. Image data powers many applications, from face recognition to manufacturing defect detection to diagnostic imaging. Videos Video data also come from camera sensors in raw format and consist of a series of frames stored as .mp4, .mov, or other video file formats. MP4 is a standard in machine learning applications due to its smaller file size, similar to .jpg for image data. Video data enables applications like autonomous vehicles and fitness apps. 3D Data (LiDAR, Radar) 3D data helps models overcome the lack of depth information from 2D data such as traditional RGB camera sensors, helping machine learning models get a deeper understanding of a scene. LiDAR (Light Detection and Ranging) is a remote sensing method that uses light to generate precise 3D images of scenes. LiDAR data is stored as point clouds in raw format and the .las file format and are often converted to JSON file format for processing by machine learning applications. Radar (Radio Detection and Ranging) is a remote sensing method that uses radio waves to determine an object's distance, angle, and radial velocity relative to the radar source. Audio Typically stored as .mp3 or .wav file formats, audio data enables speech recognition for your favorite smart assistant and real-time multilingual machine translation. 0:00 transcription Text Text data made of characters representing information, often stored in .txt, .docx, or .html files. Text powers Natural Language Processing (NLP) applications such as virtual assistants when they answer your questions, automated translation, text-to-speech, speech-to-text, and document information extraction. Why is Data Annotation Important? Machine learning powers revolutionary applications made possible by vast amounts of high-quality data. To better understand the importance of data labeling, it is critical to understand the different types of machine learning: supervised, unsupervised, and reinforcement learning. Reinforcement Learning leverages algorithms to take actions in an environment to maximize a reward. For instance, Deepmind’s AlphaGo used reinforcement learning to play games against itself to master the game of GO and become the strongest player in history. Reinforcement learning does not rely on labeled data but instead maximizes a reward function to achieve a goal. Supervised Learning vs. Unsupervised Learning Supervised learning is behind the most common and powerful machine learning applications, from spam detection to enabling self-driving cars to detect people, cars, and other obstacles. Supervised learning uses a large amount of labeled data to train a model to accurately classify data or predict outcomes. Unsupervised learning helps analyze and cluster unlabeled data , driving systems like recommendation engines. These models learn from features of the dataset itself, without any labeled data to "teach" the algorithm the expected outputs. A common approach is K-means clustering, which aims to partition n observations into k clusters and assign each observation to the nearest mean. While there are many fantastic applications for unsupervised learning, supervised learning has driven the most high-impact applications due to its high accuracy and predictive capabilities. Machine learning practitioners have turned their attention away from model improvement to improving data, coining a new paradigm: data-centric ai. Only a tiny fraction of real-world ML systems are composed of ML code. More high-quality data and accurate data labels are necessary to power better AI. As the methods to create better machine learning models shift to data-centricity, it is essential to understand the entire process of a well-defined data pipeline, from data collection methods to data labeling to data curation. This guide focuses on the most common types of data labels and the best practices for high quality so that you can get the most out of your data and therefore get the most out of your models Security at Scale.com | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Security at Scale At Scale, our customers trust us to develop reliable systems for their most important applications. This trust is our driving principle — we embed security into our platform at every level. Our Commitment Security built into every layer Protect Our Customers' Data. We are dedicated to safeguarding the data and integrity of our customers' critical work. Our security program is designed to protect customer assets and proactively reduce the frequency of negative security events. Secure Our Foundation. We treat security as foundational, integrating it deeply into our company culture and product development lifecycle. 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Products Solutions Research Resources Log in Book demo Book demo ← Blog General How Morgan Stanley deploys AI that actually works (hint: it's evals) | Human in the Loop: Episode 13 By Monica Mishra · September 16, 2025 · 24 min read Copy Link Today on Human in the Loop, Kaitlin Elliott, who leads firmwide Generative AI Solutions at Morgan Stanley, joined Clemens Viernickel and Sam Denton in the studio to unpack how AI evaluations powered the firm’s successful adoption of production GenAI. We also react to some AI Hot Takes, of course. Morgan Stanley was among the first major financial institutions to experiment with GenAI, all the way back in 2022 before it hit the headlines. Their journey offers a valuable real-world case study in moving from early pilots to enterprise-wide deployment. The team talks through the evaluation frameworks and set up they used, as well as how Kaitlin drove adoption and confidence for the application through the organization. If you’re working on rolling out a GenAI application at your enterprise, you don’t want to miss this one. About Kaitlin Elliott Kaitlin is an Executive Director in the Firmwide Artificial Intelligence division based in New York City. She leads the Firmwide Generative AI Solutions Team, which focuses on implementing cutting-edge Generative Artificial Intelligence (GenAI) models at Morgan Stanley. In this capacity, she is accountable for establishing rigorous protocols to assess generative AI model outputs and ensure alignment with organizational goals. She graduated from Providence College with a Bachelor of Arts in History and has been with Morgan Stanley since 2015. About Human in the Loop We share our learnings from working with the largest enterprises, foundation model builders, and governments to equip you with the insights you need to build practical, powerful AI systems in your enterprise. Watch the video or read the transcript below to get their insights from working with leading enterprises and frontier model labs. Episode 13 - AI Evaluations in Practice Clemens: My first question is, in the wealth management and banking space more generally, it seems that Morgan Stanley is a first mover and was very early in adopting this technology. Why would you say it's so essential for the firm to be at the forefront? Kaitlin: I would say it's been part of our DNA for a long time to be investing in technology. For the last couple of years, the firm has taken a focus on that. So in early 2022, we came across this new generative AI technology, which, as you all probably know, when it first came out, was the magic of the demo. Back then, our executives saw the capability of it being able to write a poem, and they were just blown away by that. As a firm, we’re always thinking about how we can invest in technology to ensure our employees have the best tools possible, but it's also important that our clients have the best technology available to them. For generative AI, leadership took a gamble in those early days. It was well before ChatGPT and before much of the world knew about the technology, but they decided to lean in. Our first use case was ready to go because we have been dealing with the knowledge management problem for a long time, which many enterprises have. We had already started to invest a lot into that problem. We had gone on a data journey to curate our content, to make sure it was up to date, and also to ensure it was tagged appropriately. We had been working in the conversational AI space for a while, where we had virtual assistants servicing both our employees and our clients. The gap was that it took us several years to put 10,000 FAQs together. When we first started the generative AI journey, those assistants could only answer about 10 to 20% of the questions that were asked of them. All those years, all that effort, and 10,000 FAQs, and we still weren't covering what our employees really needed from a knowledge management perspective. When we saw generative AI technology, we decided to see if we could take all of our internal knowledge in our wealth management space. We specifically focused on procedures, process documents, and our research reports. When we first saw GPT—I think we were using GPT-3—we thought we could quite literally just give it the documents and it would get it right. We were totally in the dark when we set up our first use case. After a while of experimenting, we realized that we had built what is now RAG, but we didn't know what RAG was. Almost immediately, we were able to discover that the coverage we had was significant. That's when we had the "aha" moment that this might work. It's when we decided we had to go all-in and invest here to make sure the firm would be on the forefront. Sam: Do you think the infrastructure and data you had set up for the knowledge problem was a red herring as an excuse to lean into it? Or do you think it allowed you to move quickly in the early days and make something useful in a short amount of time? Kaitlin: It was probably a combination. I think that for many corporations at the time generative AI started to become big, there was a lot of hesitation. We were well-positioned from an appetite perspective, but we also had the problem that we could solve and experiment with. And we were well-positioned with our data. The foresight is pretty incredible when you think about the journey we had already been on. Sam: That makes a lot of sense. Clemens: You mentioned an interesting point. You were an early adopter when the models first came out. On one hand, I vividly remember it was a truly magical moment of, "Oh, technology can do that. It's amazing." At the same time, it didn't take long for people testing it out to say the models were bad at many things. This is the first time the term "hallucinating" came up, meaning the models were not good enough for use in production. I'm fascinated by what led the firm to lean in and adopt this for a crucial problem, even when it seemed more like a toy than a productivity tool to many people. How did that dynamic play out? Kaitlin: Great question. I would say that when we first set it up—and what I love about the generative AI space, as you guys can appreciate, is that the demos are fantastic. Everybody has an amazing product in a demo, but when you try to apply it and have it consistently perform, it's just not there a lot of the time. Clemens: We have an episode on reacting to demos. Kaitlin: There you go. Next time I come on, we can do that. When we first set it up and started to ask a few questions, we were a little surprised that it worked. In those first instances, we asked procedure questions like, "How do I open up an account for my client?" It was able to give us a response, and we were like, "Wow, that's pretty good." But then we recognized fairly early that my team didn't have the subject matter expertise. The answer looked good to me, but I wasn't a financial advisor, so I didn't actually know if it was useful. Our first assistant was a wealth management knowledge management system, and since I wasn't a subject matter expert (SME), I didn't know if the steps it provided to open an account were all correct, or if a missing step was a critical one. So what we did very early on was create an experimental lab environment. The first thing we did was grab a bunch of SMEs and end-users—financial advisors and their support staff—and we asked them to go in and play around with this new tool. Using an assistant wasn't new to them because they already had one, but this generative AI was. We had them go in, ask a bunch of questions, and then rate for us what was good and bad about it. If something was bad, we thematically started to bucket it. Was it because it had an inaccurate answer? Was it because it was incomplete and missed a few steps? Was it because it completel Guide to Computer Vision Applications | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo All Guides ← Guide 05 Guide to Computer Vision Applications Understand what computer vision is, how it works, and deep dive into some of the top applications for computer vision by industry. April 9, 2026 28 min read Contents Introduction What is computer vision? How does computer vision work? Notable research in computer vision What are some subfields of computer vision? What are the top computer vision use cases by industry? Conclusion Additional Resources Share Introduction As discussed in our Authoritative Guide to Data Labeling , machine learning (ML) has revolutionized our approach to solving problems in computer vision and natural language processing. This guide aims to provide an overview of computer vision (CV) applications within the field of machine learning: what it is, how it works, subfields of computer vision, and a breakdown of computer vision use cases by industry. What is computer vision? For decades, people have dreamed of developing machines with the characteristics of human intelligence. An important step in creating this artificial intelligence is giving computers the ability to “see” and understand the world around them. Computer Vision is a field of artificial intelligence that focuses on developing systems that can process, analyze, and make sense of visual data (images, videos, and other sensor data) similar to the way humans do. From an engineering perspective, computer vision systems not only seek to understand the world around them but aim to automate the tasks the human visual system can perform. How does computer vision work? Computer vision is inspired by the way human visual systems and brains work. The computer vision algorithms we use today are based on pattern recognition, training models on massive amounts of visual data. For example, suppose we train a model on a million images of flowers. The system will analyze the million images, identify patterns that apply to all flowers, and at the end will learn to detect a flower given a new image. A type of deep learning algorithm called a convolutional neural network (CNN) is critical to powering computer vision systems. A CNN consists of an input layer, hidden layers, and an output layer, and these layers are applied to find the patterns described above. CNNs can have tens or even hundreds of hidden layers. Computer vision applications can be trained on a variety of data types, including images, videos, and other sensor data such as light detection and ranging (LiDAR) data, and radio detection and ranging (RADAR) data. Each data type has its strengths and shortcomings. Images Pros: Large-scale open-source datasets are available for image data ( ImageNet , MS COCO , etc.). Cameras are inexpensive if you need to collect data from scratch. Images are easier to annotate compared to other data types. Cons: Even the most popular large-scale datasets have known quality issues and gaps that can limit the performance of your models. If your use case requires depth perception (e.g. autonomous vehicles or robotics), images alone may not provide the accuracy you need. Static images alone are not sufficient to develop object-tracking models. Videos Pros: Again, cameras are inexpensive if you need to collect data from scratch. Enables the development of object tracking or event detection models. Cons: More challenging to annotate compared to images, especially if pixel-level accuracy is required. LiDAR What is LiDAR? LiDAR uses laser light pulses to scan its environment. When the laser pulse reaches an object, the pulse is reflected and returned to the receiver. The time of flight (TOF) is used to generate a three-dimensional distance map of objects in the scene. Pros: LiDAR sensors are more accurate and provide finer resolution data than RADAR. Allows for better depth perception when developing computer vision systems. LiDAR can also be used to determine the velocity of a moving object in a scene. Cons: Advancements in LiDAR technology have brought down costs in the last few years, but it is still a more costly method of data collection than images or videos. Performance degrades in adverse weather conditions such as rain, fog, or snow. Calibrating multiple sensors for data collection is a challenge. Visualizing and annotating LiDAR data is technically challenging, requires more expertise, and can be expensive. RADAR What is RADAR? RADAR sensors work much like LiDAR sensors but use radio waves to determine the distance, angle, and radial velocity of objects relative to the site instead of a laser. Pros: Radio waves have less absorption compared to the light waves used by LiDAR. Thus, they can work over a relatively long distance, making it ideal for applications like aircraft or ship detection. RADAR performs relatively well in adverse weather conditions such as rain, fog, or snow. RADAR sensors are generally less expensive than LiDAR sensors. Cons: Less angularly accurate than LiDAR and can lose sight of target objects on a curve. Less crisp/accurate images compared to LiDAR. Notable research in computer vision Advancements in the field of computer vision are driven by robust academic research. In this chapter, we will highlight some of the seminal research papers in the field in chronological order. ImageNet: A large-scale hierarchical image database J. Deng, W. Dong, R. Socher, L. -J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848. Why it’s important: This paper introduced the Imagenet dataset, which has been the standard in the field of computer vision since 2009. AlexNet : A. Krizhevsky, I. Sutskever, and Geoffrey Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems (NIPS), 2012. Why it’s important: This paper put convolutional neural networks (CNNs) on the map as a solution to solve complicated vision classification tasks. ResNet : K. He, X. Zhang, S. Ren, Jian Sun, “Deep Residual Learning for Image Recognition,” arXiv, 2015. Why it’s important: This paper introduced key ideas to help train significantly deeper CNNs. Deeper CNNs are crucial to improving the performance of computer vision models. MoCO : K. He, H. Fan, Y. Wu, S. Xie, and Ross Girshick, “Momentum Contrast for Unsupervised Visual Representation Learning,” 2020 IEEE Conference on Computer Vision and Pattern Recognition, 2019. Why it’s important: This was the first self-supervised learning paper that was competitive with supervised learning (and sparked the field of contrastive learning). Vision Transformers : A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and Neil Houlsby, “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” 2021 International Conference on Learning Representations, 2020. Why it’s important: This paper showed how transformers, which were already dominant in natural language models, could be applied for vision. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis : B. Mildenhall, P. Srinivasan, M. Tancik, J. Barron, R. Ramamoorthi, and Ren Ng, “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,” 2020 European Conference on Compter Vision, 2020. Why it’s important: This was a foundational paper for hundreds of papers in the last few years showing how to generate novel views of a 3d scene using a small number of captured images, representing the entire scene implicitly (as opposed to using classical computer graphics representations such as meshes and textures). Masked Autoencoders : K. He, X. Chen, S. Xie, Y. Li, P. Dollar, and Ross Girshick, “Masked Careers at Scale AI | Build the Future of AI | Scale AI Please rotate your device for the best experience. Guide to AI for eCommerce | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo All Guides ← Guide 04 Guide to AI for eCommerce This guide details the main applications of Artificial Intelligence for the eCommerce Industry. April 9, 2026 13 min read Contents Introduction AI for eCommerce: Why is it important? AI in eCommerce: Main Use Cases How to implement AI for eCommerce Conclusion Share Introduction 81% of retail executives say AI is at least moderately to fully functional in their organization. However, 78% of retail executives surveyed state it is hard to keep up with the evolving AI landscape . In recent years, eCommerce teams have accelerated the need to adapt to new customer preferences and create exceptional digital shopping experiences. AI adoption is no longer a choice but a necessity for retailers to drive growth at scale and maintain market differentiation. eCommerce companies are now using AI to create new forms of customer engagement, enhance online checkout solutions, and drive cost-effective processes for digital commerce. This guide will provide a comprehensive overview of the main applications for AI in eCommerce companies and share best practices from Scale’s experience in retail. AI for eCommerce: Why is it important? There are several ways AI is beneficial for eCommerce: Enhance the customer experience: AI solutions for eCommerce can help companies personalize product recommendations, improve search results, and better understand customer sentiment. With accurate personalization and recommendation machine learning models, companies can help reduce time to buy, accurately portray products on product detail pages, and better understand customer behavior. With an investment in accurate ML models, teams can achieve goals of increasing shopping conversion rates and higher customer satisfaction. In addition, eCommerce companies can increase trust and safety by removing content that violates platform guidelines, from user-generated content to merchant-specific data. Maximize profitability: ML models can help deliver accurate and targeted product recommendations based on shopping and browsing history and segment customer profiling for more accurate advertising. Teams can better understand the content and product landscape By enriching content metadata with AI. This enables eCommerce companies to focus better on product and content growth efforts and narrow in on trends early. Accelerate Operational Processes: Shopping and content trends move quickly where manual operational processes are too slow. Accelerate operational processes such as new merchant onboarding, demand forecasting, and content optimization. Techniques such as human-in-the-loop can augment machine learning models for human-level accuracy and quality. Existing processes without AI do not scale to meet the changing needs of consumers. There are three key challenges that eCommerce marketplaces face: The cost and investment are exponential: Using in-house operations teams alone to manage eCommerce data and activate new products can often inhibit growth. Manual operations to source, clean, and enrich data are time-consuming. Generating new product assets, such as product descriptions and product photography, is costly. Lack of attribute data: Personalization systems are limited by sparse attribute data. Product data may include incorrect information, duplicates, and missing attributes leading to poor search and product recommendations. Insufficiently detailed content metadata on user behavior leads to content recommendation systems that fall short. Manual processes are too slow: Consumer behaviors and content trends move quickly. Current systems require too much time and process to discover and surface trending content, and platforms fall behind on retaining customer engagement and conversion. In this guide, we’ll explain the main use cases to help solve these challenges and provide a roadmap to help grow your business with AI. AI in eCommerce: Main Use Cases There are many different applications for AI in eCommerce. In this guide, we will focus on six main categories for data-centric applications in eCommerce: Search, Advertising, and Discovery Demand Forecasting and Inventory Management Chatbots and Customer Service Content Understanding Enriched Product Data AI-Generated Product Imagery 1. Search, Advertising, and Discovery Strong customer experience starts with highly personalized recommendations, targeted product offers, and search relevance. There are three main use cases for personalized recommendations with AI: Search relevance and item discovery: 49% of online purchasers scroll past the first page to look for what they want. Search and item discovery are key components to improving the customer shopping experience, and helping customers find the right product. AI-powered search engines use natural language processing (NLP) to process and understand the query. The search engine then uses the meaning to present best ranking search results. With AI-powered search relevance, eCommerce teams can better understand the true intention behind a search term and surface the most relevant results for a customer. Ad and offer recommendations: Based on search, browse, add to cart, and purchase history, retailers can deliver targeted advertising and offers. Retailers can use machine learning to capture customer data, synthesize insights, and deliver a personalized shopping experience. Machine learning recommender systems use a recommender function, which takes information about the user including their browsing and purchase history, and predicts the rating the user will assign to a given product. Better enhanced data can aid in brands looking to deliver advertising and offers to customers. Targeted advertising aids in new customer acquisition and helps re-engage customers who may have abandoned their cart. Product recommendations: For commerce teams who are looking to lift product sales, product recommendations are key to improving ROI. ML models analyze purchasing history and build lookalike customer audiences to deliver personalized product recommendations. For example, ML models can provide recommendations for similar products, products frequently purchased together, or products bought from lookalike audiences. Product recommendations add value to retailers by encouraging repeat purchases and increasing the average order value. 2. Demand Forecasting and Inventory Management AI applications for supply chain management and logistics can dramatically accelerate processes in the global supply chain. There are three main use cases for supply chain management with AI: Demand Forecasting: One of the greatest challenges in supply chain management is demand volatility. AI-powered demand forecasting uses machine learning algorithms to predict and recognize changes in consumer demand. ML algorithms use both historical time series data, such as pricing and promotions, and any associated data such as product features and categories to determine relationships in large datasets. This allows eCommerce teams to recognize demand patterns and forecast future demand fluctuations to reduce inventory loss. Inventory management: Accurate AI-enabled demand forecasting has significant downstream impact on inventory management. Improved forecasting can lead up to 65% reduction in lost sales due to inventory that is out of stock. In addition to creating more accurate inventory, AI can help streamline aspects of warehouse management using Internet of Things (IoT) devices. With IoT, retailers can optimize warehouse operations and shipping processes with real-time inventory control. Dynamic Pricing: With improved demand forecasting and inventory management, retailers can also set dynamic pricing to increase profit. Dynamic pricing enables teams to shift from traditional, manual static pricing to pricing that changes in real-time. AI About Scale AI | Reliable AI for Critical Decisions | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo About Our mission is to develop reliable AI systems for the world's most important decisions. We provide high-quality data and full-stack technologies that power the world's leading models and enable enterprises and governments to build, deploy, and oversee AI applications that deliver real impact. 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Book a Demo Trusted AI that powers national priorities and data sovereignty Scale powers national AI transformation with Government-grade data and rigorous model evaluation for tailored AI solutions on proven infrastructure Forward Deployed Engineering Scale partners with leading governments and enterprises worldwide to turn policy ambition into AI reality. The foundations of Sovereign, Secure, and Reliable AI Scale brings together data infrastructure, model evaluation, and systems engineering to accelerate the full AI lifecycle — from strategy and data readiness to deployment and long-term operations Data End-to-End AI Data Engineering Applications Use case implementation Talent Upskilling & Enablement How Scale Delivers Value Sovereign-by-Design Architecture Built on secure infrastructure compliant with national security requirements and data mandates Proven National-Scale Expertise Experience supporting mission-critical AI for governments and public institutions Embedded Local Presence On-the-ground, multilingual teams, including Arabic-speaking talent to ensure sustained capability transfer and long-term ownership Upskilling and Enablement Accelerate capability building and iteration through embedded collaboration, upskilling, and enablement support Human-in-the-Loop Assurance Expert oversight combined with automation to deliver secure, accurate, and scalable AI outputs End-to-End Transformation From strategy and data foundations to deployment, enablement, and ongoing operations Don’t just take our word for it “ The entire region is racing to turn AI ambition into reality. 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Products Solutions Research Resources Log in Book demo Book demo ← Blog Research SWE-Bench Pro: Raising the Bar for Agentic Coding By The Scale Research Team · September 19, 2025 · 6 min read Copy Link AI agents for software engineering are rapidly advancing, but are benchmarks keeping up? With frontier models scoring so highly on SWE-Bench Verified , we wanted to raise the bar and develop a more realistic, contamination-resistant, human-augmented benchmark. SWE-Bench Pro picks up where SWE-Bench Verified leaves off, with more diverse tasks, increased difficulty, and code that models have not yet seen. On SWE-Bench Pro, the same four frontier models lead the pack, but at considerably lower scores. A Benchmark to Meet Today’s Coding Needs SWE-Bench Pro was designed to accurately measure the ability of coding agents to meet the needs of today. It contains: 1,865 total instances (731 public, 858 held-out, and 276 commercial) across 41 repositories (11 public, 12 held-out, and 18 from enterprise startups). SWE-Bench Pro solves several key challenges in evaluating AI coding agents: Data Contamination The Problem: Many benchmarks use code that models have likely seen during training. This makes it difficult to know if a model is genuinely solving a problem or just recalling a memorized solution. The Solution: We use code that models haven't been trained on. This is sourced from public codebases governed by strong copyleft licenses (e.g., GPL), whose "viral" nature and legal complexities make them highly likely to be excluded from training data, and completely private, commercial codebases from Scale's internal assets. Limited Task Diversity The Problem: Current benchmarks fail to capture the full spectrum of real-world software engineering challenges, often focusing on simple utility libraries. The Solution: We source tasks from a diverse portfolio of complex repositories, including consumer-facing applications, B2B services, and developer tools. Each repository contributes 50-100 tasks to ensure models must genuinely understand the code, not just overfit to a single project's style. Oversimplified Problems & Unrealistic Difficulty The Problem: Previous benchmarks tend to filter out ambiguous or underspecified issues, which doesn't reflect a real developer's workflow. The Solution: We preserve these challenging tasks. Because a developer's original commit messages are often unstructured or incomplete, we use a human-augmented process to enhance them. Human experts produce a clear problem statement and a list of requirements that specify the expected behavior but not how to implement the solution, preserving the core technical challenge. These tasks require substantial changes, averaging 107.4 lines of code across 4.1 files. Unreliable and Irreproducible Testing The Problem: Without a consistent setup, it's hard to know if a solution works or if the environment is just configured incorrectly. The Solution: Results: SWE-Bench Verified vs. SWE-Bench Pro We ran frontier models on Pro using the SWE-Agent scaffold and here’s what we found (all charts reflect the public dataset): Massive Performance Drop on SWE-Bench Pro: A major finding is the significant drop in performance for all models when moving from the SWE-Bench Verified benchmark to the more challenging SWE-Bench Pro. While most top models score over 70% on the verified version, the best-performing models, OpenAI GPT-5 and Claude Opus 4.1, score only 23.3% and 23.1% respectively on SWE-Bench Pro. This highlights the increased difficulty and realism of the new benchmark. The Private Commercial Subset is Harder: The private commercial subset of the SWE-Bench Pro leaderboard reveals a drop in performance. Claude Opus 4.1 decreases from 22.7% to 17.8% resolution, and OpenAI GPT-5 falls from 23.1% to 14.9%. This shows that evaluation on private, previously unseen codebases provides a more realistic measure of generalization. Significant Performance Gaps Between Models: There is a wide performance disparity among the tested AI models. Frontier models substantially outperform older models like OpenAI GPT-4o (4.9%) and DeepSeek Qwen-3 32B (3.4%). This suggests that the advanced capabilities of the latest models are critical for tackling these complex, real-world software engineering tasks. Performance Varies by Programming Language: Models show different success rates depending on the programming language. Go and Python tasks generally have higher resolution rates, with some models exceeding 30%. In contrast, performance on JavaScript (JS) and TypeScript (TS) is more varied and often lower, with rates ranging from almost 0% to over 30% depending on the specific model. Repository-Specific Difficulty: Model performance is heavily influenced by the specific repository the task comes from. Some repositories proved consistently difficult for all models, with resolve rates below 10%. On other repositories, certain models could achieve success rates higher than 50%. This indicates that factors like codebase complexity, problem type, or documentation quality significantly impact an agent's ability to succeed. Top Models are More Consistent: The highest-performing models, Claude Opus 4.1 and OpenAI GPT-5, not only achieve the highest scores but also demonstrate more stable performance across the different languages and repositories. Smaller models tend to have more "erratic" performance, succeeding moderately on some repositories while failing almost completely on others. This suggests that top models have more robust and generalizable problem-solving skills, a quality that average scores alone don't fully capture. What SWE-Bench Pro Results Mean For Developers & Engineering Leaders: Use these results to plan deployments strategically; since an agent's success varies significantly by programming language and repository complexity, you can target the specific teams and codebases where the technology will be most effective. Given that even top agents still struggle with the majority of non-trivial tasks, ensure human oversight and review remain a critical part of your workflow. The only true measure of an agent's utility is its performance on your own internal repositories. For AI Researchers: SWE-Bench Pro establishes a new, more difficult baseline that measures true generalization over memorization. The performance drop on the commercial codebase subset is a critical finding, demonstrating that current models are less capable at solving novel problems in commercial codebases. Future research must prioritize the key failure modes identified here: navigating large, unfamiliar codebases, executing high-precision edits across multiple files, and overcoming the specific complexities of ecosystems like JavaScript and TypeScript. Progress on old benchmarks is no longer a sufficient measure of advancement. Learn More To provide the clearest possible picture of model performance, we will be maintaining two separate leaderboards: A public leaderboard showing performance on tasks from the public, copyleft repositories. A commercial leaderboard reporting results exclusively on the tasks from our private, commercial codebases, serving as a measure of true generalization. Read the full research paper . Access the dataset and environments . View the live leaderboards . Ready to break through your data bottleneck? Scale's team will match your project to the right experts, fast. 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Scale AI Agentic Solutions for Insurance | Scale.com | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Turn Insurance Documents Into Intelligence Deploy AI agents to transform unstructured claims data, reports, and documents into actionable insights that prevent claims leakage, reduce customer churn, and eliminate costly manual document processing. Book a Meeting Trusted by the world's most ambitious enterprises. Meet our customers → Insurance's 3 Most Expensive Blind Spots Every insurer faces these same costly challenges: Unstructured Data Chaos Critical insights trapped in PDFs, reports, and invoices that aren't AI-ready and can't be analyzed at scale Hidden Benefits Waste Small fraction of cases analyzed for leakage, leaving companies to miss millions of dollars in recoverable waste patterns Vanishing Expertise Expert knowledge disappears when adjusters leave, creating costly training gaps How Smart Insurers Turn Data Chaos Into Unfair Advantage We directly solve the three biggest challenges facing insurance operations with purpose-built solutions that transform documents, detect waste, and preserve expertise. View all applications → Document Intelligence Transformation Transform messy unstructured reports and phone calls into machine-native structured data AI-Powered Pattern Recognition Scale case analysis with AI that identifies benefits waste patterns across thousands of documents simultaneously Knowledge Capture & Scaling Technology Capture your best adjusters' decision-making patterns and scale that expertise across every team member and claim Fuel Your AI Strategy Learn how we support AI Agents for Insurance Scale AI Agentic Claims Processing Accelerate claims with AI that works alongside your team, delivering fast, accurate outcomes. Scale AI Autonomous Customer Retention Stop customer churn before it happens with AI agents that predict issues and act proactively, not reactively. The future of insurance starts here Book a Demo Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy Training and Building Machine Learning Models | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo All Guides ← Guide 03 Training and Building Machine Learning Models The Foundational Guide April 9, 2026 39 min read Contents Training Models for Machine Learning What Are Machine Learning (ML) Models? What types of data can ML models be trained on? What are some common classes or types of ML models? What are some commonly used models? What are some common layer types in ML models? ML Modeling Frameworks/Libraries Choosing Model Metrics Best Practices and Considerations for Training Models Conclusion Additional Resources Share Training Models for Machine Learning As we presented in our previous Authoritative Guide to Data Labeling , machine learning (ML) has revolutionized both state of the art research, and the ability of businesses to solve previously challenging or impossible problems in computer vision and natural language processing. Predictive models, trained on vast amounts of data, now have the ability to learn and detect patterns reliably, all without being specifically programmed to execute those tasks. More broadly, ML models can predict numerical outcomes like temperature or a mechanical failure, recognize cars or retail products, plan better ways to grasp objects, and generate useful and helpful, salient and logical text, all without human involvement. Want to get started training and building models for your business use case? You’ve come to the right place to learn how model training works, and how you too can start building your own ML models! What Are Machine Learning (ML) Models? ML models typically take “high-dimensional” sets of data artifacts as inputs and deliver a classification, a prediction, or some other indicator as an output. These inputs can be text prompts, numerical data streams, images or video, audio, or even three-dimensional point cloud data. The computational process of producing the model output is typically called “inference,” a term adopted from cognitive science. The model is making a “prediction” based on historical patterns. What distinguishes a ML model from simple heuristics (often conditional statements) or hard-coded feature detectors (yes, face recognition used to depend on detecting a specific configuration of circles and lines!) is a series of “weights,” typically floating point numbers, grouped in “layers,” linked by functions. The system is trained through trial and error, adjusting weights to minimize error (a metric typically referred to as “loss” in the ML world) over time. In nearly all ML models, there are too many of these weights to adjust them manually or selectively; they must be “trained” iteratively and automatically, in order to produce a useful and capable model. Ideally, this model has “learned” on the training examples, and can generalize to new examples it hasn’t seen before in the real world. Because these weights are iteratively trained, the ML engineer charged with designing the system in most cases can only speculate or hypothesize about the contribution of each individual weight to the final model. Instead, she must tweak and tune the dataset, model architecture, and hyperparameters. In a way, the ML engineer “steers the ship” rather than micromanaging the finest details of the model. The goal after many rounds of training and evaluation (known as “epochs”) is to induce the process to reduce model error or loss (as we mentioned above) closer and closer to zero. Typically when a model “converges,” loss decreases to a global minimum where it often stabilizes. At this point, the model is deemed “as good as it’s going to get,” in the sense that further training is unlikely to yield any performance improvements. Sometimes it’s possible to detect that a model’s performance metrics have stabilized and engage a technique known as “early stopping.” It doesn’t make sense to spend additional time and compute spend on additional training that doesn’t meaningfully improve the model. At this stage, you can evaluate your model to see if it’s ready for production or not. Real-world user testing is often helpful to determine if you’re “ready” to launch the product that encapsulates your model, or you need to continue tweaking, adding more data, and re-training. In most applications, externalities will cause model failures or drift, requiring a continued process of maintenance and improvement of your model. Divvying up your data In order to train a model that can properly “generalize” to data it has never seen before, it’s helpful to train the model on most on 50-90% of available data, while leaving 5-20% out in a “validation” set to tune hyperparameters, and then also save 5-20% to actually test model performance. It’s important not to “taint” or “contaminate” the training set with data the model will later be tested on, because if there’s identical training assets between train and test, the model can “memorize” the result, thereby overfitting on that example, compromising its ability to generalize, which is typically an important attribute of nearly every successful ML model. Some researchers refer to the test set (that the model has never seen before) as the “hold-out” or “held out” set of data. You can think of this as the “final exam” for the model, which the model shouldn’t have seen verbatim before exam day, even if it has seen similar examples in the metaphorical problem sets (to which it has checked against the answer key) during prior training. What types of data can ML models be trained on? Tabular data If you’re simply interested in computer vision, or some of the more sophisticated and recent data types that ML models can tackle, skip ahead to the Computer Vision section. That said, working with tabular data is helpful to understand how we arrived at deep learning and convolutional neural networks for more complex data types. Let’s begin with this simpler data type. Tabular data typically consists of rows and columns. Columns are often different data types that correspond to each row entry, which might be a timestamp, a person, a transaction, or some other granular entry. Collectively, these columns can serve as “features” that help the model reliably predict an outcome. Or, as a data scientist, you can choose to multiply, subtract, or otherwise combine multiple columns and train the model on those combinations. For tabular data, there are a wide variety of possible models one can apply, to predict a label, a score, or any other (often synthesized) metric based on the inputs. Often, it’s helpful to eliminate columns that are “co-linear,” although some models are designed to deprioritize columns that are effectively redundant in terms of determining a predictive outcome. Tabular data continues the paradigm of separating training and test data, so that the model doesn’t “memorize” the training data and overfit—regurgitate examples it has seen, but fumble its response to ones it hasn’t. It even enables you to dynamically shift the sections of the table that you’ll use (best practice is to randomize the split, or randomize the table first) such that test, train, and evaluation sets are all in windows that can be slid or swapped across your dataset. This is known as cross-fold validation, or n-folds validation, where n represents the number of times the table is “folded” to divvy up your training and test sets in different portions of the table. Source: Wikipedia A final point about tabular data that we’ll revisit in the computer vision section is that data often has to be scaled to a usable range. This might mean scaling a range of values from 1 to 1000000 into a floating point number such that the range is between 0 and 1.0 or -1.0 and 1.0. Machine Learning engineers often need to experiment with different types of scaling (perhaps logarithmic is also useful for some datasets) in order for the model to reach its mo Scale GenAI Platform | Scale AI | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Reliable infrastructure is at the core of what we do. Scale GenerativeAI Platform gives teams the tools to build, deploy, and continuously improve agents that reason over your data, run reliably at scale, and get smarter every time they're used. Scroll to explore SGP 0 of 4 Connect Data Connect to enterprise data from your sources, wherever they live. Our engine ingests, labels, and structures it, getting it AI-ready with your data staying right where it is. SGP 0 of 4 Build + Execute Deploy, host, and orchestrate agents that reason over your data. Implementation supports long-running async workflows, multi-agent coordination, and any model (again with no vendor lock-in). SGP 0 of 4 Evaluate + Monitor Automated and human feedback is received to manage, debug, and improve agent performance through semantic layer monitoring, evaluation scoring, and full trace transparency, so you get visibility in real time. SGP 0 of 4 Learn + Improve Human feedback is ingested back into the model engine as a learning signal. Operations telemetry becomes training data, making the agent self-improving over time, so your systems keep pace with the frontier without rebuilding from scratch. Run your entire agent lifecycle in one place. Most teams stitch together various tools and call it a stack. SGP gives you a unified foundation – from data pipelines to live agent monitoring – built to work cohesively from day one. Scroll to explore You own the stack. Scale works inside it. SGP works agnostically across your current tools, frameworks and models. No migrating. No switch in providers. Your data Connect to data sources like Confluence, SharePoint, S3, and more. SGP structures that data through optimized bespoke pipelines, not a generic one. Your Cloud Deploy securely within your own VPC. Full support for AWS, Azure, and GCP with enterprise-grade governance at every layer. Your models Test, fine-tune, and deploy across all major models — OpenAI, Google, Meta, Mistral, and more. Switch without rebuilding. Optimize without starting over. Agents are easy to build and hard to trust. (That’s where we come in.) All Your Data. AI-Ready. Every agent is built and tested against your specific enterprise standards — your workflows, your rules, your definition of good — before it ever touches production. Agent execution + operation Scale manages the full complexity of running agents at enterprise scale — long running, async, and multi-agent workflows — so your team can focus on outcomes, not operations. Reliable and trustworthy deployment Every agent that goes into production comes with a full audit trail, source-cited outputs, and enterprise-specific oversight built in so you always know what your AI did and why. The more you use it the smarter it gets. SGP captures behavioral data from institutional knowledge, encodes expert judgement, continuously improves agent performance over time. The result: agents that make better outputs, without requiring manual retraining cycles. PERFORMANCE OVER TIME THE FLYWHEEL PROCESS 00 | 04 Usage becomes data Every query, decision, and outcome is captured and structured automatically. Nothing is wasted. Human in the loop Capture human feedback and transform raw usage signals into structured, high-quality data. Data fuels improvement Real usage data is converted into targeted improvements; no manual work, no waiting. Improvement compounds Higher-quality agents get used more. More usage generates better data. The cycle continues. Your Dialect emerges from the feedback loop. Shaping AI to think like your best employees. Learn more At its core, Scale AI Dialect is a decision map that sets you apart from the competition. It doesn’t just track how data flows, but how decisions are made. As your decision layer improves, the ‘why’ is learned and encoded by expert judgements, eventually becoming as autonomous as your guardrails allow it. SGP Platform Differentiators Capabilities Scale AI SGP Open Source In House Builds Model Builder Long-running async agents SGP’s Agentic Infrastructure layer (Agentex and AgentOps) natively enables long-running agents designed for complex tasks. Open source frameworks lack the managed infrastructure and enterprise support needed for reliable, long-running async agents at production scale. Success depends on your team’s internal bandwidth and expertise to build and maintain this infrastructure. Continuous learning flywheel With SGP, your models and systems improve over time through ongoing human feedback and co-developed IP you own. Open source tooling can support feedback loops, but requires significant internal investment to build pipelines that continuously improve model quality. Building a reliable learning flywheel is complex and resource-intensive. We’ve found this is the kind of nested complexity that stalls in-house efforts. Possible, but requires significant custom infrastructure. Most teams struggle to sustain it over time. Built-in evaluation & benchmarking Eval-driven development is core to reliable AI. We draw on our expertise in deploying for governments and on access to SEAL, our in-house frontier benchmarking lab. Enterprise compliance & governance Scale's partnership model includes compliance, data governance, and IP ownership structures suited for regulated industries. Open source tools have no built-in compliance framework. You're responsible for all security, governance, and regulatory requirements. Human-in-the-loop oversight Reliable AI is built around human + AI collaboration, which is baked into SGP. Security that holds up to government standards View safety standards + DoD IL4 Provisional Authorization SOC 2 Type II ISO 27001 FedRAMP High Authorized Trusted by industry leaders “ We have a lot more to do. We have an exciting roadmap ahead that we will be announcing shortly, and we're going to continue to be partnering with Scale AI, and I'm really excited about that. ” “ We wanted to not just stand up a demo or POC, but deploy production-ready use cases and infrastructure. With Scale GenAI Platform, we were able to quickly launch our first use case: a GenAI solution that makes it easy for users across Global Atlantic to get information out of our Enterprise Data Hub. This is enabling data-driven decision making and shortening the time to insights from days or weeks down to seconds. ” “ Scale AI is really the backbone of our AI success. It's critical and I'm excited to keep building forward that relationship and delivering more for Howard Hughes. ” Jessica Sibley Padma Elmgart Carlos Olea See how SGP can transform your business Our team will walk you through SGP in the context of your actual environment not a generic demo. Talk to an expert Frequently Asked Questions Is there any vendor lock-in? Scale's flexible, interoperable, and largely open-source technology toolkit ensures no platform lock-in, with your enterprise retaining full ownership of their data and agent code. Talk to an expert Will I still own my IP? Yes. You retain full ownership of your data, business logic, and any custom AI solutions we build for you. Scale owns the underlying platform infrastructure and technical frameworks, but your solution is yours. No vendor lock-in, ever. Talk to an expert How do I measure reliability? Scale's GenAI data engine provides high-quality, production-aligned evaluation datasets in days, ensuring models and agentic applications perform as expected. Talk to an expert What is safeguarding against bad actors? Scale’s expertise in model behavior uniquely positions us to ensure application reliability in a way that competitors cannot. We are the industry leading experts in GenAI red teaming. Talk to an expert Dive deeper Product Introducing Dialect: The Missing Layer Between AI and Enterprise Trust Re Meet with our team | Scale AI | Scale AI Please rotate your device for the best experience. Products Solutions Research Resources Log in Book demo Book demo Let's Scale Together Join leading AI teams accelerating their ML development with Scale. Book a 1:1 demo with us to get started. “ We're going to need a lot more investment in high-quality evals and benchmarks to help us understand the actual comparative utility of the various models. This new set of private evals and leaderboards from Scale are great to see ” Nat Friedman Entrepreneur and Investor Trusted by the world's most ambitious AI teams Products Scale data engine Scale GenAI Platform Scale Donovan Solutions Enterprise Insurance Healthcare US Public Sector Global Public Sector Company About Careers Security Terms Privacy Modern Slavery Statement Resources Blog Contact Us Events Documentation Guides Data Labeling ML Model Training Diffusion Models Guide to AI for eCommerce Computer Vision Applications Large Language Models Reliable AI for the world’s most important decisions Manage your cookie preferences Copyright © 2026 Scale AI, Inc. All rights reserved Terms of Use & Privacy Policy