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H2O MLOps

4 posts · Feb 2026 – Jun 2026

What this trail covers

H2O MLOps provides a comprehensive platform for managing, deploying, governing, and monitoring machine learning models in production. It streamlines the end-to-end model lifecycle, from experimentation to production deployment and ongoing monitoring. Key capabilities include model management and registry, 3rd party model ingestion, collaborative experiment repositories, model versioning, various deployment modes (real-time, batch, A/B testing), multi-environment support, automated scaling, and model packaging for seamless deployment via MLflow.

All H2O.ai trails
  1. H2O MLOpsThis 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.
  2. Fine-tuned LLM deployment with MLflowThis post introduces a production-ready pattern for packaging and deploying fine-tuned binary classification LLMs using MLflow. It details how to create MLflow-compatible model artifacts by wrapping the LLMStudio output (base model, classification head weights, config files) within a custom MLflow PyFunc. The PyFunc's load_context method handles GPU/CPU selection and artifact loading, while the predict method implements the inference pipeline including tokenization, forward pass, logit extraction, classification head application, and sigmoid activation for probability output. The design is extensible to multi-class/multi-label scenarios.