MLflow
An open-source platform for managing the ML lifecycle—from experiments to production deployment.
Think of it like
Git for machine learning: track every model version, roll back, compare metrics.
Example
Log a training run with MLflow, it records code, params, metrics, and the model artifact. Later, register it, promote it to staging, deploy to prod. Full audit trail.
How it actually works
MLflow has four components: Tracking (logs runs), Projects (reproducible execution), Models (unified format), Registry (deployment workflow). It's agnostic to the framework and runs on your infrastructure or their cloud. Widely adopted in enterprises; plays well with Databricks.
For product teams
Critical for model governance and compliance; enables safe promotion of models through stages.
For engineers
Run-level granularity; model registry with versioning and transitions.
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