Real Time ML Drift Detection & Monitoring via H2O MLOps | Part 11

How H2O MLOps and Apache Superset track model performance and calculate data drift in production deployments. Models degrade over time as real-world data distributions shift. H2O MLOps allows teams to configure baseline metrics and select specific columns for drift monitoring the moment a deployment begins scoring. For deeper analysis, integrated Apache Superset provides a visualization layer where teams can build custom dashboards using stored statistical aggregates—bin edges, counts, and sums—enabling granular drift calculations across both continuous and categorical features over time. ➤ Real-Time Model Monitoring: Configure baseline metrics and track live predictions directly within the H2O MLOps interface. 🔗 https://docs.h2o.ai/mlops/model-monitoring ➤ Advanced Drift Detection & Custom Dashboards: Calculate feature distribution drift and build analytics dashboards using integrated Apache Superset. 🔗 https://docs.h2o.ai/mlops/model-monitoring ➤ Granular Metrics Capture: Monitor statistical aggregates including bin counts, edges, and categorical feature statistics for precise drift analysis. 🔗 https://docs.h2o.ai/mlops/model-monitoring