H2O MLOps

What this post added

This post introduces H2O MLOps, detailing its capabilities for operating AI models with transparency, scale, and confidence. It covers model management and registry (including 3rd party ingestion and versioning), model deployment (modes, environments, updates/rollbacks), model monitoring (drift, accuracy, fairness, operational metrics, custom thresholds), team collaboration, and governance (lineage, reproducibility, runtime explanations). It also outlines infrastructure considerations like high availability and Kubernetes configuration, and deployment options (Fully Managed Cloud, Hybrid).