To improve reproducibility and collaboration in the ML development workflow, we should add DVC (Data Version Control) to the mlops setup. DVC will manage datasets and possibly model artifacts, enabling better tracking of changes and seamless sharing across the team.
Proposal:
- Integrate DVC into the MLops pipeline.
- Configure DVC to track relevant data and model files.
- Update documentation to cover usage instructions for DVC.
- Ensure compatibility with existing tools and workflows as MLflow.
Benefits:
- Enhanced reproducibility for ML experiments.
- Easier collaboration and sharing of results.
- Efficient storage and management of large files.
To improve reproducibility and collaboration in the ML development workflow, we should add DVC (Data Version Control) to the mlops setup. DVC will manage datasets and possibly model artifacts, enabling better tracking of changes and seamless sharing across the team.
Proposal:
Benefits: