Version Control and Reproducibility

Version control is crucial for managing model updates and code changes. Key strategies include:

Git for code: Track changes to scripts, APIs, and configurations.

Model versioning: Store multiple versions of models with metadata (training data, metrics, parameters).

Environment tracking: Save Python dependencies or Docker images to ensure reproducibility.

Benefits:

Rollback to previous versions if a new model performs poorly.

Maintain experiment history and compare models easily.

Example:

• MLflow or DVC can manage model versions alongside datasets for reproducible deployment.