What is ML model versioning and why is it important?

What is ML model versioning and why is it important?

ML model versioning is the practice of managing different versions of machine learning models. It's important for tracking changes, ensuring reproducibility, and facilitating collaboration.

How do I implement versioning in my ML projects?

You can implement versioning by using tools like DVC (Data Version Control) or MLflow, which help track model changes and datasets.

What are the best practices for ML model versioning?

Best practices include maintaining clear documentation, using semantic versioning, and automating the versioning process with CI/CD pipelines.

Can you give me an example of a versioning strategy?

A common strategy is to use a major.minor.patch format, where major changes are breaking changes, minor changes add functionality, and patches are for bug fixes.