Model registry & deployment
Deploy and manage models in production
Streamline your ML workflows with MLflow's comprehensive model registry for version control, approvals, and deployment management.
Model registry
Stage-based model lifecycle management
Move models through customizable staging environments (Development, Staging, Production, or any stage alias you choose) with built-in approval workflow capabilities and automated notifications. Maintain complete audit trails of model transitions with detailed metadata about who approved changes and when they occurred.
Model deployment flexibility
Deploy models as Docker containers, Python functions, REST endpoints, or directly to various serving platforms with MLflow's versatile deployment capabilities. Streamline the transition from development to production with consistent model behavior across any target environment, from local testing to cloud-based serving.
Model serving
Scalable Real-Time Serving
Databricks Model Serving provides a unified, scalable interface for deploying models as REST APIs that automatically adjust to meet demand fluctuations. With managed deployment on Databricks, your endpoints can intelligently scale up or down based on traffic patterns, optimizing both performance and infrastructure costs with no manual intervention required.
High-Performance Batch Inference
Deploy production models for batch inference directly on Apache Spark, enabling efficient processing of billions of predictions on massive datasets
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