Summary

In this chapter, we expanded our view beyond H2O ML at scale, which has been the focus of this book to this point. We did this by introducing H2O's end-to-end ML platform called H2O AI Cloud. This platform has a broad set of components in the model building and model deployment steps of the ML life cycle and introduces a lesser-considered layer to this flow – easy-to-build AI applications and an App Store to serve them. We learned that H2O AI Cloud has four specialized engines for building ML models – DistributedML, AutoML, DeepLearningML, and DocumentML. We learned that MLOps has a full capability set around deploying, monitoring, managing, and governing models for scoring. We also learned that a Feature Store is available to centralize and reuse features for model building and model scoring.

Importantly, we learned that the focus of this book, building ML models on massive datasets and deploying to enterprise systems for scoring (what we have called...