H2O MLOps

This post details the capabilities of H2O MLOps, focusing on model management and registry (including 3rd party model ingestion and leaderboards), model deployment (with modes like A/B testing, multiple environments, and real-time/batch serving), model monitoring (covering drift, accuracy, fairness/bias, and operational metrics), team collaboration and management, governance and compliance (lineage, reproducibility, explanations at runtime), and infrastructure options (high availability, Kubernetes configuration). It highlights automated scaling, automated drift detection, and model explanations at runtime as key features for operating AI models with transparency, scale, and confidence.