Managing ML Artefacts with DVC¶
Data Version Control (DVC) is a command line tool that enables version control for ML artefacts (e.g., models and training datasets), using a Git repository and a filesystem (e.g., cloud object storage).
This demo is based around version control for a dataset, but it would work in exactly the same way for any ML model serialised to a file.
- How to initialise version control for a dataset stored on AWS S3.
- How to update a dataset.
- How to fetch any versions of dataset.
Running the Demo¶
This demo is contained within a single Jupyter notebook -
demos/dvc/data_and_model_versioning.ipynb. Make sure you have the necessary Python package requirements installed into a Jupyter kernel for it to run successfully.