Streamlining ML Workflows: Automating Hyperparameter Tuning with CI/CD and DVC

Machine Learning development is complex. Unlike traditional software, it involves managing data dependencies, model versioning, and dynamic experimentation. Implementing a robust CI/CD (Continuous Integration/Continuous Delivery) pipeline is essential to automate these experiments and ensure high-quality delivery.

In this post, I’ll walk through how to combine Data Version Control (DVC) with GitHub Actions to track hyperparameter tuning automatically.

1. The Role of CI/CD in Machine Learning

CI/CD in ML goes beyond standard code testing. It enables us to automate model building, manage data versioning, and run experiments seamlessly. By integrating tools like DVC, we can treat ML experiments (like changing a hyperparameter) just like code commits.

2. Tracking Metrics with DVC

To effectively tune hyperparameters, you need to track how they affect model performance. DVC allows you to configure your dvc.yaml file to specifically track metrics files (like metrics.json) alongside your plots.

For example, your dvc.yaml might look like this to cache false for metrics, ensuring they are always regenerated during reproduction:

stages:
  train:
    cmd: python train.py
    deps:
      - data/processed
      - train.py
    outs:
      - confusion_matrix.png
    metrics:
      - metrics.json:
          cache: false

3. Automating the Comparison (The "Diff")

The real power comes when you tweak a hyperparameter (e.g., changing max_depth or n_estimators) and rerun your pipeline. Instead of manually checking results, DVC provides a command to compare experiments directly.

dvc metrics diff

4. CI/CD Integration with GitHub Actions

You can automate this entire process using GitHub Actions. By using the iterative/setup-dvc action, you can replace manual script execution with a standardized DVC pipeline.

A typical workflow involves:

1. Setting up DVC in the runner.
2. Running the pipeline with dvc repro .
3. Generating a report using dvc metrics diff --md and posting it as a comment on your Pull Request using CML (Continuous Machine Learning).

Conclusion

By leveraging DVC and CI/CD, you move from "guessing" which hyperparameter worked best to having a concrete, automated report attached to every code change. This ensures that every improvement to your model is tracked, verifiable, and reproducible.