frank-luongt/skills/codex/databricks-model-serving

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name: databricks-model-serving

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 |

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Quick Start: Deploy a GenAI Agent

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

%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!"}]
)

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Quick Start: Deploy a Classical ML Model

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.

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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 |

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Common Workflows

Check Endpoint Status After Deployment

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

Returns:

{
    "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}
    ]
)

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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:

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

CORRECT - use helper methods:

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

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Resources

• Model Serving Documentation
• MLflow 3 ResponsesAgent
• Agent Framework

<!-- Source: .faos/custom/skills/cloud/databricks/databricks-model-serving/SKILL.md -->