data-versioning-patterns

What problem does it solve?

This Skill addresses the critical challenge of managing and versioning datasets in machine learning projects, ensuring reproducibility and auditability.

Core Features & Use Cases

• Version Control for Data: Integrates with tools like DVC to track datasets alongside code.
• Reproducible Pipelines: Defines ML workflows in dvc.yaml for end-to-end reproducibility.
• Experiment Management: Facilitates hyperparameter tuning and experiment tracking without cluttering Git history.
• Time Travel for Datasets: Leverages Delta Lake for accessing historical data versions.
• Lineage Tracking: Supports OpenLineage for emitting metadata about data processing jobs.
• Use Case: Ensure that a specific model version can be retrained with the exact same data it was originally trained on, or audit the data used for a particular prediction.

Quick Start

Initialize DVC in your Git repository and track a dataset named 'raw_data.csv' by running dvc init followed by dvc add raw_data.csv.