databricks-model-serving

To deploy MLflow models to Databricks Model Serving, you register the model and expose it as a scalable REST API endpoint. This enables programmatic access to retrieve model predictions from deployed endpoints.

Yes, you can build conversational agents using MLflow 3's ResponsesAgent interface and deploy them to Databricks Model Serving. This provides scalable REST API access to query AI-powered conversational agents.

Yes, Databricks Model Serving supports integration with Unity Catalog Functions and Vector Search. This enhances deployed agent capabilities by combining model serving with unified governance and retrieval functionality.

The best way to query a deployed MLflow model on Databricks is by sending REST API requests to the Model Serving endpoint. This allows you to retrieve predictions or agent responses programmatically.

Deploying conversational agents on Databricks uses MLflow 3's ResponsesAgent interface and supports LangGraph integration. LangGraph helps structure the conversational logic before deploying to REST API endpoints.