databricks-model-serving

To deploy machine learning models to Databricks Model Serving, you can deploy classical ML models, custom pyfunc models, or GenAI agents directly to production-ready endpoints. The deployment workflow supports automated authentication via Databricks resources and comprehensive logging.

Yes, you can deploy GenAI agents built with LangGraph and Databricks Foundation Models to a serving endpoint. These deployed agents support tool augmentation by integrating directly with Unity Catalog Functions and Vector Search for real-time data access.

You can query deployed AI agents and ML models on Databricks using SDKs, REST APIs, or MCP tools. This enables seamless integration of your deployed serving endpoints into external web applications or downstream data pipelines.

Yes, Databricks Model Serving supports tool-augmented conversational agents by integrating Unity Catalog Functions and Vector Search. This allows deployed LangGraph or ResponsesAgent models to access and query real-time customer data during interactions.

Databricks Model Serving supports deploying classical ML models, custom Python functions (pyfunc), and sophisticated GenAI agents. It accommodates various workflows from basic model version deployment to advanced agents built with LangGraph and Foundation Models.

No, manual authentication configuration is not required. The deployment and querying workflows enable automated authentication via Databricks resources, streamlining the process of securing and accessing your deployed models and agents.