Model Versioning

Summary

Tracking which exact model artifact is deployed where, so you can roll back, compare and reproduce results — as important for ML as code versioning is for software.

How to Learn This

1Use a model registry (MLflow Model Registry or similar) to version a trained model.
2Practice tagging model versions with metadata: training data, metrics, date.
3Simulate a rollback: deploy a new version, detect a regression, revert to the previous one.

Docker for ML Models

Experiment Tracking (MLflow, Weights & Biases)

More topics in MLOps & Model Deployment

Model Serving (FastAPI/Flask)Docker for ML Models Experiment Tracking (MLflow, Weights & Biases)CI/CD for ML Pipelines Model Monitoring & Drift Detection A/B Testing Models Batch vs Real-time Inference Kubernetes for ML (Overview)Feature Stores (Overview)

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