Deploying and Monitoring Models with H2O MLOps
- Observability
- H2O MLOps
- H2O Driverless AI
This lesson walks through registering and deploying a model using H2O MLOps. It covers how registering a model captures the artifact, metadata, training metrics, and validation scores in a version-controlled registry, and how Driverless AI's scoring pipelines can run as REST endpoints, batch jobs, or deployments to external systems like Databricks and Snowflake. It shows configuring a real-time deployment's resource settings and running an A/B test or champion-challenger setup to de-risk a model update. It closes by noting that monitoring starts automatically once a deployment goes live.
Key takeaways
- How registering a model in MLOps captures its artifact, metadata, training metrics, and validation scores
- How a champion-challenger or A/B test setup lets you evaluate a new model before a full rollout
- What resource settings you configure for a real-time deployment, including replicas and vertical autoscaling
- What monitoring tracks automatically once a deployment goes live: request volume, latency, error rates, and drift
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