What are the best practices for using MLflow and DVC together?

What are the best practices for using MLflow and DVC together?

When using MLflow and DVC together, it's essential to keep your data versioning and model tracking organized. Start by defining a clear workflow that integrates both tools.

Can you elaborate on how to set up the integration?

Sure! First, ensure that DVC is set up for data versioning. Then, use MLflow to log your experiments and models. You can link DVC data paths in your MLflow tracking.

What are some common pitfalls to avoid?

Common pitfalls include not versioning your data consistently, neglecting to document your experiments, and failing to synchronize DVC and MLflow configurations.

How can I automate the workflow between MLflow and DVC?

You can automate the workflow by using CI/CD pipelines that trigger DVC commands for data updates and MLflow commands for logging experiments.