Databricks provides an extensive MLOps framework to enhance the machine learning lifecycle, encompassing development, deployment, and monitoring. It features automated model deployment using Python, notebooks, and SQL, executed through Azure Data Factory for consistency. Although it allows flexible development with its toolkit, it does not impose a standardized ML processing framework, offering flexibility. Model lineage is tracked with Unity Catalog and MLFlow, categorizing models by versions and tracking detailed experiment data. The model serving capabilities include real-time inference with autoscaling and configurable endpoints for A/B testing, with upcoming GPU support and integration with Azure ML available. Monitoring utilizes configurable SQL dashboards for comprehensive oversight of model performance and health, with support for custom metrics;