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Welcome to Michelangelo

Michelangelo is an end-to-end ML platform for building, deploying, and managing machine learning models. Born at Uber — where it powers 25,000+ model trainings per month and ~30 million predictions per second — now open source.

Get started​

I'm evaluating Michelangelo​

Understand what the platform does, how it compares to your current stack, and whether it fits your use case.

  • Overview — What Michelangelo is, how it works, and how familiar tools map to it
  • Core Concepts — Projects, workflows, tasks, and the key terms you'll encounter

I want to build my first pipeline​

Get a local environment running and build an end-to-end ML pipeline.

I'm deploying or operating the platform​

Set up infrastructure, configure compute clusters, and deploy the UI.

I want to contribute​

What Michelangelo is — and isn't​

Understanding scope helps you decide if Michelangelo is the right tool.

Michelangelo is:

  • An ML lifecycle platform — data prep, training, evaluation, deployment, and monitoring in one system
  • An orchestration framework (Uniflow) for writing ML pipelines as Python code with @task and @workflow decorators
  • A model registry for versioning, tracking, and managing trained models
  • A deployment system for online inference (Triton) and batch predictions
  • A no-code UI (MA Studio) for standard ML workflows without writing code

Michelangelo is not:

  • A notebook environment — use Jupyter/Colab for exploration, then bring your code to Michelangelo for production
  • A data warehouse — it connects to your existing data sources (S3, Snowflake, BigQuery, HDFS)
  • A general-purpose workflow engine — it's purpose-built for ML, not arbitrary DAGs
  • A model monitoring SaaS — monitoring is built in, but Michelangelo is self-hosted infrastructure, not a managed service
  • A replacement for your ML framework — use PyTorch, TensorFlow, XGBoost, scikit-learn as you normally would