---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 7b371f7e186042bdff0daffebf43c7377e125122db304c8185485b1389262788
  pageDirectory: concepts
  sources:
    - tracing-autogen-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-databricks-tracking
    - MDT
  citations:
    - file: tracing-autogen-databricks-on-aws.md
title: MLflow Databricks Tracking
description: Setting up MLflow to use the Databricks tracking URI and experiments for capturing and visualizing agent traces
tags:
  - mlflow
  - databricks
  - monitoring
timestamp: "2026-06-19T23:11:05.365Z"
---

# [MLflow](/concepts/mlflow.md) Databricks Tracking

**MLflow Databricks Tracking** is the integration of [MLflow Tracking](/concepts/mlflow-tracking.md) with the Databricks platform, enabling users to log, query, and visualize machine learning experiments directly within the Databricks environment. This integration provides a seamless experience for tracking model training runs, parameters, metrics, and artifacts when working with Databricks workspaces. ^[tracing-autogen-databricks-on-aws.md]

## Overview

[MLflow Tracking](/concepts/mlflow-tracking.md) is a component of [MLflow](/concepts/mlflow.md) that records and queries experiments, including parameters, metrics, and artifacts. When used with Databricks, the tracking URI is set to `"databricks"`, which automatically configures [MLflow](/concepts/mlflow.md) to log experiments to the Databricks workspace's managed [MLflow](/concepts/mlflow.md) server. This eliminates the need to manually configure a remote tracking server and provides tight integration with Databricks features like experiment management and the Runs UI. ^[tracing-autogen-databricks-on-aws.md]

## Configuration

To use [MLflow Tracking](/concepts/mlflow-tracking.md) with Databricks, you configure the tracking URI and set the experiment within your code:

```python
import [[mlflow|MLflow]]

# Set the tracking URI to use [[databricks-managed-mlflow|Databricks-Managed MLflow]]
[[mlflow|MLflow]].set_tracking_uri("databricks")

# Set or create an experiment in the Databricks workspace
[[mlflow|MLflow]].set_experiment("/Shared/my-experiment")
```

After this configuration, all subsequent [MLflow Tracking](/concepts/mlflow-tracking.md) calls (such as `mlflow.log_param()`, `mlflow.log_metric()`, `mlflow.log_artifact()`) are recorded in the specified experiment within the Databricks workspace. ^[tracing-autogen-databricks-on-aws.md]

## Auto Tracing Integration

[MLflow on Databricks](/concepts/mlflow-on-databricks.md) supports auto tracing for various frameworks, automatically capturing and logging [Traces](/concepts/traces.md) to the experiment. This is enabled by calling the appropriate `autolog()` function for the framework being used. ^[tracing-autogen-databricks-on-aws.md]

### AutoGen Auto Tracing

For [AutoGen](/concepts/autogen-auto-tracing.md) workflows, you can enable [Automatic Tracing](/concepts/automatic-tracing.md) with:

```python
import [[mlflow|MLflow]]

# Turn on auto tracing for AutoGen
[[mlflow|MLflow]].autogen.autolog()

# Set up [[mlflow-tracking|MLflow Tracking]] on Databricks
[[mlflow|MLflow]].set_tracking_uri("databricks")
[[mlflow|MLflow]].set_experiment("/Shared/autogen-tracing-demo")
```

When auto tracing is enabled, [MLflow](/concepts/mlflow.md) automatically captures the multi-agent conversation flow, tool calls, and responses generated by AutoGen agents. This provides visibility into the complex interactions between agents without requiring manual instrumentation. ^[tracing-autogen-databricks-on-aws.md]

## Key Features

- **Managed Tracking Server**: The Databricks workspace provides a fully managed [MLflow Tracking](/concepts/mlflow-tracking.md) server, eliminating infrastructure management overhead. ^[tracing-autogen-databricks-on-aws.md]
- **Experiment Organization**: Experiments are organized within the Databricks workspace file system (e.g., `/Shared/experiment-name`), making them accessible to workspace users. ^[tracing-autogen-databricks-on-aws.md]
- **Framework Auto-Logging**: [Automatic Tracing](/concepts/automatic-tracing.md) is available for frameworks like [AutoGen](/concepts/autogen-auto-tracing.md), LangChain, PyTorch, and other popular ML frameworks. ^[tracing-autogen-databricks-on-aws.md]
- **Trace Visualization**: [Traces](/concepts/traces.md) of agent interactions and tool calls are visualized in the Databricks [MLflow](/concepts/mlflow.md) UI, allowing inspection of complex workflows. ^[tracing-autogen-databricks-on-aws.md]

## Authentication

[MLflow on Databricks](/concepts/mlflow-on-databricks.md) uses the Databricks authentication configured in the environment (such as personal access tokens or OAuth) when the tracking URI is set to `"databricks"`. Environment variables like `OPENAI_API_KEY` or other LLM provider keys must be set separately for model inference calls. ^[tracing-autogen-databricks-on-aws.md]

## Example Workflow

A typical workflow combining [MLflow](/concepts/mlflow.md) Databricks Tracking with AutoGen auto tracing:

1. Configure [MLflow Tracking](/concepts/mlflow-tracking.md) to use Databricks: `mlflow.set_tracking_uri("databricks")`
2. Set the experiment path: `mlflow.set_experiment("/Shared/my-experiment")`
3. Enable auto tracing: `mlflow.autogen.autolog()`
4. Define and run the AutoGen agent workflow
5. View the logged [Traces](/concepts/traces.md), parameters, and metrics in the Databricks [MLflow](/concepts/mlflow.md) UI

This integration allows you to monitor and debug complex multi-agent systems while leveraging Databricks for experiment management and collaboration. ^[tracing-autogen-databricks-on-aws.md]

## Related Concepts

- [MLflow Tracking](/concepts/mlflow-tracking.md) — The core [MLflow](/concepts/mlflow.md) component for experiment logging
- [MLflow Experiments](/concepts/mlflow-experiment.md) — Organizational units for [MLflow](/concepts/mlflow.md) runs
- [MLflow Auto Logging](/concepts/mlflow-autologging.md) — Automatic logging for various ML frameworks
- [AutoGen Tracing](/concepts/autogen-auto-tracing.md) — Tracing specifically for AutoGen multi-agent workflows
- Databricks Notebooks — The primary interface for running [MLflow Tracking](/concepts/mlflow-tracking.md) workflows
- [MLflow Runs UI](/concepts/mlflow-run.md) — The Databricks interface for viewing experiment runs and [Traces](/concepts/traces.md)

## Sources

- tracing-autogen-databricks-on-aws.md

# Citations

1. [tracing-autogen-databricks-on-aws.md](/references/tracing-autogen-databricks-on-aws-04b86736.md)
