Manage Models at Scale with H2O MLOps
- Observability
- H2O MLOps
- H2O Driverless AI
This lesson walks through model management as an AI program scales from a handful of models to dozens or hundreds in production. It covers how H2O MLOps' model repository stores a complete profile for each registered model, including training metrics, validation scores, feature importance, and a link back to the original experiment. It shows how full version history supports reproducibility and governance, and how MLflow models with their package dependencies can be imported and deployed the same way as native models. It closes by noting tags let teams organize a growing model portfolio by business unit, use case, or risk level.
Key takeaways
- What a registered model's profile includes beyond a scoring artifact, from training metrics to feature importance
- How full version history in H2O MLOps supports reproducibility and governance over time
- Why H2O MLOps isn't locked to native models, since MLflow models can be imported with their dependencies
- How third-party models get deployed and monitored through the same infrastructure as native H2O models
- The tags and taxonomy options help organize a growing model portfolio by business unit or risk level
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