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MLOps and machine learning

End to end ML pipeline

advanced

train, register, serve, and monitor a model

Status

Track where this project stands in your portfolio.

Suggested stack

MLflow, FastAPI, Docker, Evidently

Proves

the full MLOps lifecycle

Milestones
  1. 01pipeline
  2. 02registry
  3. 03serving
  4. 04drift monitoring
  5. 05retrain trigger