skills.rest
ScottHMcKean
ScottHMcKeanCOMMUNITY
Explore all skills by this author →
GitHub

databricks-model-serving

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

3|Updated Jan 2, 2025
Search Tags:#deployment#mlflow#databricks#api endpoints#model serving#genai agents
One-click install
npx skills add https://github.com/ScottHMcKean/genai_notebooks --skill databricks-model-serving-scotthmckean
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-model-serving
Source: https://github.com/ScottHMcKean/genai_notebooks/tree/main/.cursor/skills/databricks-model-serving
Command: npx skills add https://github.com/ScottHMcKean/genai_notebooks --skill databricks-model-serving-scotthmckean

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the deployment and querying of machine learning models and generative AI agents on Databricks, enabling scalable and accessible AI solutions.

Core Features & Use Cases

  • Model Deployment: Deploy classical ML models (sklearn, xgboost) and custom Python functions (pyfunc) to production-ready REST API endpoints.
  • GenAI Agent Deployment: Build and deploy sophisticated conversational agents using LangGraph and MLflow's ResponsesAgent, integrating tools like Unity Catalog Functions and Vector Search.
  • Endpoint Querying: Interact with deployed models and agents using Python SDK, REST API, or MCP tools for seamless integration into applications.
  • Use Case: A data science team can use this Skill to deploy a customer churn prediction model and an AI assistant that answers customer support queries, both accessible via API endpoints.

Quick Start

Use the databricks-model-serving skill to deploy the 'main.models.my_classifier' model 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 machine learning models to Databricks Model Serving endpoints?▼

You can deploy classical ML models like sklearn and xgboost, or custom Python functions (pyfunc), to production-ready REST API endpoints using Databricks Model Serving.

Can I deploy GenAI agents with tool integration on Databricks?▼

Yes, you can build and deploy GenAI agents using LangGraph and MLflow's ResponsesAgent, integrating tools like Unity Catalog Functions and Vector Search for sophisticated conversational applications.

What is the best way to query deployed ML models and GenAI agents?▼

Query deployed models and agents by interacting with the Databricks Model Serving endpoints using the Python SDK, REST API, or MCP tools for seamless application integration.

Does Databricks Model Serving work with MLflow for model deployment?▼

Yes, Databricks Model Serving works with MLflow to deploy models and GenAI agents, utilizing MLflow's ResponsesAgent to build sophisticated conversational agents with tool integration.

How do I expose a custom pyfunc model as a REST API endpoint?▼

You can expose a custom pyfunc model as a REST API endpoint by using Databricks Model Serving to deploy custom Python functions to production-ready endpoints.

Related Skills

Explore More Software Engineering Skills→
thbehthbeh

databricks-model-serving

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

Community
Advanced
vrcmsvrcms
1

exa-search

Retrieves real-time web and code search results via the Exa MCP server.

Community
Intermediate
teegin-gteegin-g

databricks-model-serving

Deploy MLflow models and GenAI agents to Databricks model-serving endpoints.

Community
Advanced
PaldomPaldom
11

databricks-model-serving

Deploy and query Databricks Model Serving endpoints for MLflow models and GenAI agents.

Community
Advanced