Agent skill

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.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/model-serving-datasciencemonkey-claude-code-cli-bric

SKILL.md

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
Custom Python model mlflow.pyfunc.PythonModel 2-custom-pyfunc.md
GenAI Agent (LangGraph, tool-calling) ResponsesAgent 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 sklearn, xgboost, autolog
Custom PyFunc 2-custom-pyfunc.md Custom preprocessing, signatures
GenAI Agents 3-genai-agents.md ResponsesAgent, LangGraph
Tools Integration 4-tools-integration.md UC Functions, Vector Search
Development & Testing 5-development-testing.md MCP workflow, iteration
Logging & Registration 6-logging-registration.md mlflow.pyfunc.log_model
Deployment 7-deployment.md Job-based async deployment
Querying Endpoints 8-querying-endpoints.md SDK, REST, MCP tools
Package Requirements 9-package-requirements.md DBR versions, pip

Quick Start: Deploy a GenAI Agent

Step 1: Install Packages (in notebook or via MCP)

python
%pip install -U mlflow==3.6.0 databricks-langchain langgraph==0.3.4 databricks-agents pydantic
dbutils.library.restartPython()

Or via MCP:

execute_databricks_command(code="%pip install -U mlflow==3.6.0 databricks-langchain langgraph==0.3.4 databricks-agents pydantic")

Step 2: Create Agent File

Create agent.py locally with ResponsesAgent pattern (see 3-genai-agents.md).

Step 3: Upload to Workspace

upload_folder(
    local_folder="./my_agent",
    workspace_folder="/Workspace/Users/[email protected]/my_agent"
)

Step 4: Test Agent

run_python_file_on_databricks(
    file_path="./my_agent/test_agent.py",
    cluster_id="<cluster_id>"
)

Step 5: Log Model

run_python_file_on_databricks(
    file_path="./my_agent/log_model.py",
    cluster_id="<cluster_id>"
)

Step 6: Deploy (Async via Job)

See 7-deployment.md for job-based deployment that doesn't timeout.

Step 7: Query Endpoint

query_serving_endpoint(
    name="my-agent-endpoint",
    messages=[{"role": "user", "content": "Hello!"}]
)

Quick Start: Deploy a Classical ML Model

python
import mlflow
import mlflow.sklearn
from sklearn.linear_model import LogisticRegression

# Enable autolog with auto-registration
mlflow.sklearn.autolog(
    log_input_examples=True,
    registered_model_name="main.models.my_classifier"
)

# Train - model is logged and registered automatically
model = LogisticRegression()
model.fit(X_train, y_train)

Then deploy via UI or SDK. See 1-classical-ml.md.

MCP Tools

If MCP tools are not available, use the SDK/CLI examples in the reference files below.

Development & Testing

Tool Purpose
upload_folder Upload agent files to workspace
run_python_file_on_databricks Test agent, log model
execute_databricks_command Install packages, quick tests

Deployment

Tool Purpose
create_job Create deployment job (one-time)
run_job_now Kick off deployment (async)
get_run Check deployment job status

Querying

Tool Purpose
get_serving_endpoint_status Check if endpoint is READY
query_serving_endpoint Send requests to endpoint
list_serving_endpoints List all endpoints

Common Workflows

Check Endpoint Status After Deployment

get_serving_endpoint_status(name="my-agent-endpoint")

Returns:

json
{
    "name": "my-agent-endpoint",
    "state": "READY",
    "served_entities": [...]
}

Query a Chat/Agent Endpoint

query_serving_endpoint(
    name="my-agent-endpoint",
    messages=[
        {"role": "user", "content": "What is Databricks?"}
    ],
    max_tokens=500
)

Query a Traditional ML Endpoint

query_serving_endpoint(
    name="sklearn-classifier",
    dataframe_records=[
        {"age": 25, "income": 50000, "credit_score": 720}
    ]
)

Common Issues

Issue Solution
Invalid output format Use self.create_text_output_item(text, id) - NOT raw dicts!
Endpoint NOT_READY Deployment takes ~15 min. Use get_serving_endpoint_status to poll.
Package not found Specify exact versions in pip_requirements when logging model
Tool timeout Use job-based deployment, not synchronous calls
Auth error on endpoint Ensure resources specified in log_model for auto passthrough
Model not found Check Unity Catalog path: catalog.schema.model_name

Critical: ResponsesAgent Output Format

WRONG - raw dicts don't work:

python
return ResponsesAgentResponse(output=[{"role": "assistant", "content": "..."}])

CORRECT - use helper methods:

python
return ResponsesAgentResponse(
    output=[self.create_text_output_item(text="...", id="msg_1")]
)

Available helper methods:

  • self.create_text_output_item(text, id) - text responses
  • self.create_function_call_item(id, call_id, name, arguments) - tool calls
  • self.create_function_call_output_item(call_id, output) - tool results

Resources

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