Data And Model Versioning
Versioning is the process of uniquely naming multiple iterations of models used at different stages of ML development to track all changes.
What is Data and Model Versioning?
Versioning refers to the process of uniquely naming multiple iterations of an ML model used at different stages of ML development. It helps track and control all changes applied to various versions allowing the easy recovery of a previous model version when needed.
An example of data and model versioning
ML experiment involves different project versions with specific enhancements or changes in each version. These changes might include
- Update features
- Update parameters
- Adjust parameters
- Add the new dataset and features
- Readjust parameters
Data versioning tools allow capturing the versions of data and models and switching between different versions as needed. It offers a unified way of accessing data, code, and ML models – a complete journal of work performed. For example, check this elaborative image given in DVC user documentation.
Why is Data and Model Versioning Essential?
Helps bring reproducibility
ML versioning helps finalize the best model and its tradeoffs. It plays a crucial role in ensuring reproducibility in ML experiments. By catching the snapshot of the entire ML pipeline, it becomes easy to reproduce the same results while saving on the retraining and testing efforts and time.
Better tracking
ML workflows are error-prone and complex and hence require tracking. ML models can fail or underperform for several reasons like, adding more data or updating features. Model versioning helps revert failed models to their previous, stable, and working versions.
Track dependencies
ML experiments involve complex workflows with several elements that affect model performance. For example, datasets, frameworks, features set, test cases contribute to model performance. Model versioning helps track dependencies that affect ML model performance. It helps test multiple models in various ML pipelines, tune parameters and hyperparameters, and keep model accuracy in check.
Scaled AI-ML Governance
ML projects are rolled out iteratively for scaled performance and failure tolerance. Model versioning supports better AI governance with access control, policies, the right version deployments, and model activity tracking.
Getting ML Versioning Right
Data and model versioning is better achieved with the right set of tools. The chosen toolset should offer insights on elements of the ML pipeline and mechanism to link data, code, and model versions. The following tools are used for precise ML versioning.
Data and model versioning is an important step in the entire ML lifecycle. Precise ML versioning helps drive reproducibility and desired performance of ML systems over multiple versions.
Further Reading
Version Control For ML Models, Explained
Version Control for ML Models: Why You Need It, What It Is, How To Implement It