Scale AI

Founded 2016

Prepare training data and evaluate custom machine learning models for enterprise deployments

You can use Scale AI to train, test, and deploy machine learning models with curated data and human feedback. The service manages training data generation, dataset labeling, and fine-tuning for frontier foundation models and computer vision applications. You can benchmark model capabilities against private leaderboards, run evaluations to identify performance gaps, and audit safety risks across domain-specific use cases. Organizations also use it to build autonomous agents and integrate generative models into specialized workflows across robotics, government, and enterprise systems.

What Scale AI does

Annotate image and video datasetsRun RLHF workflows on text modelsBenchmark models against standard evaluation suitesAudit model responses for hallucinationsGenerate domain-specific synthetic training dataPrepare structured data from raw unstructured documentsTrack model accuracy metrics over timeHuman-in-the-loop data labeling and expert curationAutomated model evaluations and custom safety benchmarksRLHF and fine-tuning workflows for foundation modelsPrivate model performance leaderboards and scoringEnterprise data pipelines for multimodal datasetsSpecialized annotation for robotics, autonomous vehicles, and healthcareDomain expert contributor network for complex academic topics

Frequently asked

Weights & Biases, Amazon S3, Microsoft Azure, Databricks, Snowflake, PyTorch, Hugging Face, AWS S3 / Marketplace, Google Cloud Storage, PyTorch / TensorFlow, Slack (via Notis), Zapier, Linear, MongoDB, AWS S3, Azure Blob Storage, TensorFlow, Slack, Jira, AWS S3 / SageMaker, Google Cloud Storage / Vertex AI, Labelbox (via API)