Automating Model Retraining and Deployment in H2O
- Observability
- H2O MLOps
- H2O Driverless AI
This lesson covers how H2O MLOps manages the full model lifecycle after an experiment is built, starting with registering a new model version and noting whether it replaces an existing one. It walks through the Champion Challenger framework for testing a new model version against what is already in production, along with A/B testing and one-click deactivation for underperforming models. It then shows how a Driverless AI experiment's Python API can be turned into a retraining pipeline and scheduled to run on a set cadence, such as a weekly comparison or a continuous drift check that retrains a model automatically.
Key takeaways
- How the Champion Challenger framework lets a new model version be tested against production before promotion
- What A/B testing adds when you want to evaluate a new model side by side with an existing one
- How a Driverless AI experiment's Python code can be turned into an automated retraining pipeline
- Why scheduling a workflow to check for drift lets a model retrain and redeploy itself automatically
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