databricks-model-serving

You can deploy ML models to Databricks serving endpoints by logging them with MLflow, creating a serving endpoint, and invoking it via SDKs or MCP tools. This Skill supports classical ML, custom PyFunc, and GenAI agents.

Yes, you can deploy sophisticated GenAI agents built with LangGraph and ResponsesAgent to Databricks Model Serving. The deployment supports integrating external tools like UC Functions and Vector Search for real-time agent interactions.

Yes, Databricks Model Serving works with custom Python PyFunc models alongside classical ML frameworks like sklearn and xgboost. It facilitates the complete lifecycle from logging to endpoint creation and querying.

The best way to manage the lifecycle of MLflow models on Databricks is to use a deployment Skill that handles logging, endpoint creation, and status checking. This enables efficient model lifecycle management and scalable REST API access.

To query a deployed Databricks endpoint status and get real-time predictions, use the provided SDKs and MCP tools. These tools allow you to check deployment status and invoke the endpoint for immediate model or agent responses.