Scaling ML Deployments with H2O MLOps

This lesson walks through how H2O MLOps runs automated readiness checks before any deployment goes live, confirming the scoring pipeline is complete and its dependencies resolve, which matters for third party artifacts like MLflow objects or fine tuned small language models. It covers batch scoring with configurable scheduling for processing large volumes of records overnight, and how every deployment runs as an isolated containerized service with configurable replicas and autoscaling to handle variable load. The lesson also covers logging of model requests and responses, and building custom monitoring dashboards in Apache Superset.