Hi @hemeiling, To build MLOps on Databricks, you can manage the full machine learning lifecycle in one unified platform. Start by preparing your data using Delta Lake and notebooks. Then, train and track models using MLflow, which lets you log metrics, parameters, and artifacts. Store and reuse features with the Feature Store, and manage access and governance through Unity Catalog. Once your model is ready, deploy it with Model Serving for real-time or batch predictions. Automate everything using Databricks Workflows, CI/CD tools, and Git integration. Finally, monitor model performance and detect data drift to keep your models accurate and up-to-date.
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