What is data versioning in machine learning?

What is data versioning in machine learning?

Data versioning in ML refers to the practice of tracking and managing different versions of datasets used in model training.

Why is data versioning important?

It's crucial for reproducibility, collaboration, and ensuring that models are trained on the correct data.

How can I implement data versioning in my ML projects?

You can use tools like DVC (Data Version Control) or Git LFS to manage and version your datasets effectively.

What challenges might I face with data versioning?

Challenges include managing large datasets, ensuring consistency, and integrating versioning into existing workflows.