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Enterprise Layer Intelligence
Categories/AI Orchestration & MLOps/Snorkel AI
Snorkel AI

Snorkel AI

Founded 2019

Develop specialized training datasets and evaluation benchmarks for frontier artificial intelligence models

Pricing model

Paid

Rating

★ People love it

Time to value

Requires Expertise

You can use Snorkel AI to build custom training datasets, evaluation benchmarks, and testing environments for frontier machine learning models. The system brings together domain experts and automated checkers to create accurate labels, reasoning traces, and rubric-based evaluations. You can create specialized datasets for complex areas like law, finance, and software development where general data fails. It also lets you build runnable environments to test coding agents, evaluate computer-use agents, and verify multi-step workflows with programmatic scoring and complete data provenance.

What Snorkel AI does

Create expert-verified datasets for fine-tuning machine learning modelsSet up custom benchmark environments for autonomous agentsScore model outputs using deterministic automated gradersCalibrate human domain experts against standardized test rubricsGenerate synthetic edge-case data from expert-written seedsTrack label provenance and reviewer decisions across dataset revisionsCurriculum-structured training datasets with tiered difficulty levelsExpert reviewer calibration and scoring against research gold setsProgrammatic graders and specialized evaluator modelsAudit trails and full provenance tracking for every data labelRunnable test environments for browser, command line, and software toolsAutomated rubric failure mode diagnosticsDeterministic verification for unit tests and math calculations

Pricing breakdown

Pricing model: Paid

Tutorials & Demos

Frequently asked

AWS SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, Snowflake, Hugging Face, PyTorch, TensorFlow, MLflow, Kubernetes, Databricks Unity Catalog, PyTorch / TensorFlow, Kubernetes (AKS/EKS)

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Snorkel AI, training data, data development, AI benchmarks, model evaluation, agent evaluation, RLVR, synthetic data, data labeling, programmatic labeling, reasoning traces, frontier models, SWE-bench, OSWorld