Automated Model Training in CI/CD Pipelines
Automatically Retrain Models with GitHub Actions and DVC, and Share Evaluation Reports in Pull Requests Using CML.
By Sandip Das · · 9 min read
💡
Hey — It's Sandip Das 👋
Automated retraining to keep models updated as code and data change, without repeating manual steps.
Adding metrics and confusion matrices to pull requests helps teams review model quality and catch regressions before deployment.
Automated retraining to keep models updated as code and data change, without repeating manual steps.
Adding metrics and confusion matrices to pull requests helps teams review model quality and catch regressions before deployment.
📚 Key Learnings
- Triggering model retraining automatically
- Running
dvc reproinside GitHub Actions - CML for reporting metrics in PRs
🧠 Learn here
Automated retraining is not "cron runs python train.py". It is a closed loop with a human checkpoint in it. Something changes upstream (a new data drop, a parameter tweak, a schedule tick).
A runner reproduces the pipeline exactly the way DVC would on your laptop. The evaluation stage emits metrics and plots. CML turns those into a pull request comment.
A reviewer compares candidate against champion and merges,
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, GitHub Actions