databricks-model-serving

Name: databricks-model-serving
Availability: InStock
Author: laurentprat-db

Deploy and query ML models and GenAI agents on Databricks Model Serving.

Updated Feb 27, 2026

Search Tags:#deployment #mlflow #databricks #model serving #api endpoint #genai agents

Ready to run

npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill databricks-model-serving-laurentprat-db

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-model-serving
Source: https://github.com/LaurentPRAT-DB/LPT_claude_config/tree/main/skills/databricks-model-serving
Command: npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill databricks-model-serving-laurentprat-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the deployment of machine learning models and AI agents into scalable, production-ready REST API endpoints, enabling seamless integration into applications and workflows.

Core Features & Use Cases

Model Deployment: Deploy classical ML models (sklearn, xgboost), custom Python functions (PyFunc), and advanced GenAI agents (ResponsesAgent, LangGraph) to Databricks Model Serving.
Tool Integration: Seamlessly incorporate Unity Catalog Functions and Vector Search indexes as tools for AI agents.
Endpoint Querying: Interact with deployed models and agents using SDKs, REST APIs, or MCP tools for real-time predictions and responses.
Use Case: Deploy a customer churn prediction model to an endpoint that can be queried by a CRM system to flag at-risk customers in real-time. Or, deploy a GenAI agent that can answer customer support queries using a knowledge base.

Quick Start

Use the databricks-model-serving skill to deploy the 'main.agents.my_agent' model version 1 to a serving endpoint.

Frequently Asked Questions about databricks-model-serving

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy a machine learning model to a Databricks Model Serving endpoint?▼

To deploy a machine learning model to a Databricks Model Serving endpoint, use synchronous deployment for classical ML models like sklearn and xgboost, or custom PyFunc models, exposing them via scalable REST API endpoints for real-time predictions.

Can I deploy GenAI agents to Databricks Model Serving with tool integrations?▼

Yes, you can deploy complex GenAI agents like ResponsesAgent and LangGraph to Databricks Model Serving. This supports asynchronous job-based deployment and integrates tools such as Unity Catalog Functions and Vector Search indexes for advanced querying.

What's the best way to query a deployed ML model or GenAI agent on Databricks?▼

The best way to query deployed ML models or GenAI agents on Databricks is by using SDKs, REST APIs, or MCP tools. This enables real-time predictions and responses directly from your integrated applications and workflows.

Does Databricks Model Serving support asynchronous deployment for AI agents?▼

Yes, Databricks Model Serving supports asynchronous job-based deployment specifically for GenAI agents. This contrasts with the synchronous deployment method used for standard classical ML models and custom Python functions.

How do I expose a customer churn prediction model as an API endpoint for my CRM?▼

You can expose a customer churn prediction model as an API endpoint by using synchronous deployment to Databricks Model Serving. Once deployed, your CRM system can query this REST API endpoint to flag at-risk customers in real-time.