databricks-model-serving

To deploy machine learning models to a Databricks serving endpoint, you use the deployment functionality to host classical ML, custom pyfunc, and GenAI agents. This creates scalable REST API access for real-time application querying.

Yes, Databricks Model Serving supports deploying GenAI agents built with LangGraph and ResponsesAgent. It allows seamless integration of Unity Catalog Functions and Vector Search as tools for these deployed AI agents.

The best way to query a deployed model on Databricks using a REST API is through the provided Python SDK or MCP tools. This endpoint querying capability enables scalable access for both classical ML models and GenAI agents.

Yes, Databricks Model Serving fully supports custom Python functions and pyfunc models. You can deploy them alongside classical ML frameworks like sklearn and xgboost to scalable endpoints for real-time scoring.

You integrate Unity Catalog Functions as tools for AI agents on Databricks during the model serving deployment process. This allows GenAI agents to seamlessly query these functions alongside Vector Search tools.

You can deploy classical ML models like sklearn and xgboost, custom Python functions, and GenAI agents using Databricks Model Serving endpoints. This supports applications ranging from customer churn prediction to conversational AI.