Version Control of Local Datasets for MLflow Experiments
Data version control is indispensable for MLflow users, ensuring robust reproducibility and seamless collaboration throughout the machine learning lifecycle.
It provides the foundation for precise tracking of dataset changes, experiment configurations, and model artifacts.
In this webinar, we will
- Guide you through the process of working with data locally while effectively versioning it using lakeFS.
- Provide a hands-on experience in seamlessly integrating data version control with MLflow experimentation management.
- Demonstrate how to leverage lakeFS to create a streamlined workflow that ensures your data science projects in MLflow are not only reproducible but also highly efficient.