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MLOps Academy · Lesson

Train and Log to the Registry

Run training and register the resulting model.

One Run, Fully Captured

This lesson ties training and the registry together. You train a model, then push it into the MLflow Model Registry so it becomes a versioned, reusable artifact. 🚀

Start a Tracking Run

Wrap your training in mlflow.start_run(). Everything you log inside that block belongs to one run, with its own id and timestamp.

import mlflow

with mlflow.start_run() as run:
    # train and log here
    print(run.info.run_id)

All lessons in this course

  1. Train and Log to the Registry
  2. Promote the Best Model to Production
  3. Serve the Production Model
  4. Trace a Prediction Round-Trip
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