databricks-model-serving

Name: databricks-model-serving
Availability: InStock
Author: zack2626-ok

Deploy MLflow models and AI agents to Databricks Model Serving REST endpoints.

Updated Jun 11, 2026

Search Tags:#ai agents #mlflow #databricks #model serving #conversational agents

Ready to run

npx skills add https://github.com/Zack2626-ok/DATN_Website-Dat-Ban-Va-Quan-Ly-Nha-Hang --skill databricks-model-serving-zack2626-ok

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-model-serving
Source: https://github.com/Zack2626-ok/DATN_Website-Dat-Ban-Va-Quan-Ly-Nha-Hang/tree/main/.windsurf/skills/databricks-model-serving
Command: npx skills add https://github.com/Zack2626-ok/DATN_Website-Dat-Ban-Va-Quan-Ly-Nha-Hang --skill databricks-model-serving-zack2626-ok

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, databricks-langchain, langgraph, databricks-agents, pydantic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps deploy MLflow models and AI agents to Databricks Model Serving endpoints, enabling scalable access to machine learning models and AI-powered conversational agents.

Core Features & Use Cases

Model Deployment: Deploy MLflow models and AI agents to REST API endpoints.
Agent Creation: Build and deploy conversational agents using MLflow 3's ResponsesAgent interface.
Integration: Integrate Unity Catalog Functions and Vector Search to enhance agent capabilities.
Querying: Send requests to deployed endpoints and retrieve model predictions or agent responses.

Quick Start

Deploy a GenAI agent using the databricks-model-serving skill.

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 MLflow models to Databricks Model Serving endpoints?▼

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.

Can I build and deploy conversational AI agents using Databricks?▼

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.

Does Databricks Model Serving work with Unity Catalog Functions and Vector Search?▼

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.

What's the best way to query a deployed MLflow model on Databricks?▼

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.

Does deploying conversational agents on Databricks require LangGraph?▼

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.