Scale AI

Scale AI sits at the critical junction between raw data and production-ready machine learning — the company that built the annotation and data engine powering training datasets for some of the most advanced AI systems in the world. Founded in San Francisco in 2016 by Alexandr Wang, Scale grew from a computer vision labeling service into a comprehensive data platform handling text, audio, video, sensor fusion for autonomous vehicles, and increasingly, RLHF (reinforcement learning from human feedback) pipelines for large language model development. The US Department of Defense, major autonomous vehicle programs, and leading AI labs all became Scale customers.

The platform operates through a combination of the Tasker workforce (distributed human annotators), automated quality assurance pipelines, and the Scale Nucleus data management system. The API allows machine learning teams to submit batches of raw data — images, documents, conversations — specify annotation requirements via detailed task instructions, and receive labeled outputs with quality metadata. Nucleus provides dataset visualization, model evaluation, and slice analysis tools for understanding where models fail. Enterprise customers integrate Scale into their continuous training pipelines, automatically routing model failures or low-confidence predictions back to human review.

Service disruptions at Scale have a different tempo than typical SaaS outages because the pipeline involves both software infrastructure and human task distribution. If the API or submission portal is down, teams cannot submit new annotation tasks, pausing labeling work on training data that downstream model training schedules depend on. Nucleus becomes inaccessible, blocking dataset review and evaluation workflows. Existing task queues may stall without progress updates, making it impossible to estimate delivery timelines. For organizations running continuous learning loops — where new production data is constantly being routed to annotation — even a multi-hour disruption can create backlogs that take days to clear.

Outage.gg provides real-time status monitoring for Scale AI so ML teams can confirm platform-side issues quickly. Visit the live status page to see current incident reports.