Having established the fundamentals of versioning data with DVC and tracking experiments with MLflow in the preceding chapters, we now turn to combining these tools. Effectively integrating data management and experiment logging is key to creating truly reproducible machine learning workflows.

This chapter provides practical guidance on:

• Connecting specific data versions tracked by DVC to the corresponding experiment runs logged by MLflow.
• Structuring your ML projects to facilitate the use of both DVC and MLflow.
• Techniques for automatically logging DVC metadata within MLflow runs.
• Building and reproducing automated pipelines using DVC commands like dvc run and dvc repro.
• Incorporating MLflow tracking directly within DVC pipeline stages.
• Establishing best practices for maintaining coherent and reproducible workflows using DVC and MLflow together.

By the end of this chapter, you will understand how to build integrated systems where changes in data, code, parameters, and results are consistently tracked and managed.