DataRobot offers MLOps tools that allow enterprise organizations to monitor, manage, and govern AI models after they have been deployed to production, regardless of where or how those models were originally built. The system automatically tracks model health, detects data drift, and alerts teams when a model's performance is degrading.

DataRobot MLOps provides a centralized hub for deploying, monitoring, and governing both DataRobot-originated and externally built models. It continuously tracks service health, accuracy, and data drift—the phenomenon where incoming real-world data starts to differ from the data the model was originally trained on—using automated model competitions (challenger models) to identify when a replacement model should take over. The platform also supports regulatory compliance through audit trails, fairness monitoring, and one-click compliance documentation aligned with frameworks such as the EU AI Act and NIST RMF. Governance features include a deployment approval workflow requiring MLOps administrator sign-off before models go live.