model-serving
Deploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5) checking endpoint status. Covers classical ML models, custom pyfunc, and GenAI agents.
npx skills add majiayu000/claude-skill-registry --skill model-serving-datasciencemonkey-claude-code-cli-bric --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Databricks Model Serving Deploy MLflow models and AI agents to scalable REST API endpoints. ## Quick Decision: What Are You Deploying? | Model Type | Pattern | Reference | |------------|---------|-----------| | **Traditional ML** (sklearn, xgboost) | `mlflow.sklearn.autolog()` | [1-classical-ml.md](1-classical-ml.md) | | **Custom Python model** | `mlflow.pyfunc.PythonModel` | [2-custom-pyfunc.md](2-custom-pyfunc.md) | | **GenAI Agent** (LangGraph, tool-calling) | `ResponsesAgent` | [3-genai-agents.md](3-genai-agents.md) | ## Prerequisites - **DBR 16.1+** recommended (pre-installed GenAI packages) - Unity Catalog enabled workspace - Model Serving enabled ## Reference Files | Topic | File | When to Read | |-------|------|--------------| | Classical ML | [1-classical-ml.md](1-classical-ml.md) | sklearn, xgboost, autolog | | Custom PyFunc | [2-custom-pyfunc.md](2-custom-pyfunc.md) | Custom preprocessing, signatures | | GenAI Agents | [3-genai-agents.md](3-genai-agents.md) | ResponsesAgent, LangGraph | | Tools Integration | [4-tools-integration.md](4-tools-integration.md) | UC Functions, Vector Search | | Development & Testing | [5-development-testing.md](5-development-testing.md) | M
- Quick Decision: What Are You Deploying?
- Prerequisites
- Reference Files
- Quick Start: Deploy a GenAI Agent
- Step 1: Install Packages (in notebook or via MCP)
- Step 2: Create Agent File
- Step 3: Upload to Workspace
- Step 4: Test Agent
- Step 5: Log Model
- Step 6: Deploy (Async via Job)
- Step 7: Query Endpoint
- Quick Start: Deploy a Classical ML Model
- MCP Tools
- Development & Testing
What does the model-serving skill do?
Deploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5) checking endpoint status. Covers classical ML models, custom pyfunc, and GenAI agents.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-serving-datasciencemonkey-claude-code-cli-bric --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.