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Lesson 8 of 8

Reproducibility and MLOps

In plain words: Make every result repeatable: same data + same code + same settings = same model.
  • Version your code (Git), your data (dated folders or DVC) and your models (a model registry).
  • Log every experiment: parameters, metrics, and the data version used (MLflow or your cloud's tracker).
  • Pin package versions in requirements.txt.
  • Automate retraining with a pipeline once the model is in production — that's MLOps.
Tip If you can't reproduce last week's model, you can't safely improve it.