model-serving
OfficialDeploy and query Databricks model endpoints
System Documentation
What problem does it solve?
This Skill enables teams to deploy MLflow models and AI agents to scalable Databricks Model Serving endpoints and to query them reliably.
Core Features & Use Cases
- Deploy traditional ML models (sklearn, xgboost) and custom PyFunc models to serving endpoints.
- Deploy GenAI agents (ResponsesAgent) and LangGraph-based agents with tool-calling capabilities.
- Query endpoints, check endpoint status, and monitor deployments across development, staging, and production.
Quick Start
Install required packages and authentication for Databricks, then log a model, deploy it to a serving endpoint, and start querying the endpoint. Typical steps include installing mlflow, databricks-langchain, langgraph, databricks-agents, and pydantic; logging the model with MLflow; deploying via Databricks UI or API; and sending requests to the endpoint to receive predictions.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: model-serving Download link: https://github.com/databricks-solutions/ai-dev-kit/archive/main.zip#model-serving Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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