Model Versioning

Summary

Tracking which model version is deployed, what data and code produced it, and being able to roll back to a previous version — essential once models are updated regularly in production.

How to Learn This

1Explore a model versioning tool (e.g. MLflow) at a basic level.
2Learn what metadata is worth tracking alongside a model (training data version, hyperparameters, metrics).
3Practice a simple naming/tagging convention for your own project's model versions.

Docker Basics for ML

CI/CD for ML Pipelines (Overview)

More topics in Model Deployment & MLOps

Saving & Loading Models (Pickle, joblib)Building an API for a Model (Flask / FastAPI)Docker Basics for ML CI/CD for ML Pipelines (Overview)Model Monitoring & Drift Detection A/B Testing Deployed Models Cloud ML Platforms (SageMaker, Vertex AI Overview)

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