# Databricks Model Serving > - Skill: `frank-luongt/databricks-model-serving` (Agent Skill) - Install (CLI): `npx skillmds@latest add frank-luongt/databricks-model-serving` - Raw SKILL.md: https://api.skillmd.com/api/skills/frank-luongt/databricks-model-serving/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: frank-luongt (https://skillmd.com/u/frank-luongt) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/frank-luongt/databricks-model-serving --- --- name: databricks-model-serving tags: [cloud, databricks] --- # 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) | MCP workflow, iteration | | Logging & Registration | [6-logging-registration.md](6-logging-registration.md) | mlflow.pyfunc.log_model | | Deployment | [7-deployment.md](7-deployment.md) | Job-based async deployment | | Querying Endpoints | [8-querying-endpoints.md](8-querying-endpoints.md) | SDK, REST, MCP tools | | Package Requirements | [9-package-requirements.md](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](3-genai-agents.md)). ### Step 3: Upload to Workspace ``` upload_folder( local_folder="./my_agent", workspace_folder="/Workspace/Users/you@company.com/my_agent" ) ``` ### Step 4: Test Agent ``` run_python_file_on_databricks( file_path="./my_agent/test_agent.py", cluster_id="" ) ``` ### Step 5: Log Model ``` run_python_file_on_databricks( file_path="./my_agent/log_model.py", cluster_id="" ) ``` ### Step 6: Deploy (Async via Job) See [7-deployment.md](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](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 - [Model Serving Documentation](https://docs.databricks.com/machine-learning/model-serving/) - [MLflow 3 ResponsesAgent](https://mlflow.org/docs/latest/llms/responses-agent-intro/) - [Agent Framework](https://docs.databricks.com/generative-ai/agent-framework/)