model-serving
CommunityDeploy and query ML models and agents.
AuthorPaldom
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a comprehensive guide to deploying machine learning models and AI agents as scalable REST API endpoints on Databricks, streamlining the path from development to production.
Core Features & Use Cases
- Model Deployment: Deploy classical ML models (sklearn, xgboost), custom PyFunc models, and GenAI agents (ResponsesAgent, LangGraph).
- Tool Integration: Seamlessly integrate Unity Catalog Functions and Vector Search into AI agents.
- End-to-End Workflow: Covers development, testing, logging, registration, deployment, and querying of models and agents.
- Use Case: Deploy a customer churn prediction model or an AI assistant that answers questions about your company's knowledge base.
Quick Start
Use the model-serving skill to deploy the 'main.agents.my_agent' model version 1 to a serving endpoint.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 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/Paldom/databricks-apps-streamlit-vibe-coding-starter/archive/main.zip#model-serving Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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