Connect Your Development Environment to MLflow

This guide shows you how to connect your development environment to an MLflow Experiment. You can run MLflow on your local machine, self-host the open source MLflow service, or use a managed offering, such as Databricks Managed MLflow.

MLflow Experiments

An MLflow Experiment is the container for your GenAI application. Learn more about Experiments in the MLflow documentation.

Prerequisites

OSS MLflow

• Python Environment: Python 3.8+ with pip installed
• Local or Remote Server: Access to run MLflow tracking server

Databricks

• Databricks Workspace: Access to a Databricks workspace

Authentication Methods

This guide describes using a Databricks Personal Access Token. MLflow also works with the other Databricks-supported authentication methods.

Setup Instructions

OSS MLflow

Step 1: Install MLflow

Install MLflow for local development:

pip install --upgrade "mlflow>=3.1"

Step 2: Start MLflow Tracking Server

Option A: Local Tracking (Default)

MLflow will automatically use local file storage if no tracking URI is specified:

import mlflow

# Creates local mlruns directory for experiments
mlflow.set_experiment("my-genai-experiment")

Option B: Remote Tracking Server

Start a remote MLflow tracking server:

# Start MLflow server (in a separate terminal)
mlflow server --host 0.0.0.0 --port 5000

Then configure your client to use the remote server:

import mlflow

# Connect to remote MLflow server
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-genai-experiment")

Option C: Database Backend

For production use, configure MLflow with a database backend:

# Example with PostgreSQL
mlflow server \
    --backend-store-uri postgresql://user:password@localhost:5432/mlflow \
    --default-artifact-root s3://my-mlflow-bucket/artifacts \
    --host 0.0.0.0 \
    --port 5000

Step 3: Configure Environment (Optional)

For consistent configuration across your team, use environment variables:

# .env file
MLFLOW_TRACKING_URI=http://localhost:5000
MLFLOW_EXPERIMENT_NAME=my-genai-experiment

Load in your Python code:

import os
from dotenv import load_dotenv
import mlflow

load_dotenv()

# Set tracking URI and experiment
mlflow.set_tracking_uri(os.getenv("MLFLOW_TRACKING_URI", "file:./mlruns"))
mlflow.set_experiment(os.getenv("MLFLOW_EXPERIMENT_NAME", "default"))

Step 4: Verify Your Connection

Create a test file and run this code:

import mlflow

# Print connection information
print(f"MLflow Tracking URI: {mlflow.get_tracking_uri()}")
print(f"Active Experiment: {mlflow.get_experiment_by_name('my-genai-experiment')}")

# Test logging
with mlflow.start_run():
    mlflow.log_param("test_param", "test_value")
    print("✓ Successfully connected to MLflow!")

Step 5: Access MLflow UI

Open your browser to view the MLflow UI:

• Local tracking: http://localhost:5000 (if running mlflow server)
• File-based tracking: Run mlflow ui in your project directory, then go to http://localhost:5000

Databricks - Local IDE

Step 1: Install MLflow

Install MLflow with Databricks connectivity:

pip install --upgrade "mlflow[databricks]>=3.1"

Step 2: Create an MLflow Experiment

1. Open your Databricks workspace
2. Go to Experiments in the left sidebar under Machine Learning
3. At the top of the Experiments page, click on New GenAI Experiment

Step 3: Configure Authentication

Choose one of the following authentication methods:

Option A: Environment Variables

1. In your MLflow Experiment, click Generate API Key
2. Copy and run the generated code in your terminal:

export DATABRICKS_TOKEN=<databricks-personal-access-token>
export DATABRICKS_HOST=https://<workspace-name>.cloud.databricks.com
export MLFLOW_TRACKING_URI=databricks
export MLFLOW_EXPERIMENT_ID=<experiment-id>

Option B: .env File

1. In your MLflow Experiment, click Generate API Key
2. Copy the generated code to a .env file in your project root:

DATABRICKS_TOKEN=<databricks-personal-access-token>
DATABRICKS_HOST=https://<workspace-name>.cloud.databricks.com
MLFLOW_TRACKING_URI=databricks
MLFLOW_EXPERIMENT_ID=<experiment-id>

3. Install the python-dotenv package:

pip install python-dotenv

4. Load environment variables in your code:

# At the beginning of your Python script
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

Step 4: Verify Your Connection

Create a test file and run this code to verify your connection:

import mlflow

# Test logging to verify connection
print(f"MLflow Tracking URI: {mlflow.get_tracking_uri()}")
with mlflow.start_run():
    print("✓ Successfully connected to MLflow!")

Databricks - Notebook

Step 1: Install MLflow

Databricks runtimes include MLflow, but for the best experience with GenAI capabilities, update to the latest version:

%pip install --upgrade "mlflow[databricks]>=3.1"
dbutils.library.restartPython()

Step 2: Create a Notebook

Creating a Databricks Notebook will create an MLflow Experiment that is the container for your GenAI application. Learn more about Experiments in the MLflow documentation.

1. Open your Databricks workspace
2. Go to New at the top of the left sidebar
3. Click Notebook

Step 3: Configure Authentication

No additional authentication configuration is needed when working within a Databricks Notebook. The notebook automatically has access to your workspace and the associated MLflow Experiment.

Step 4: Verify Your Connection

Run this code in a notebook cell to verify your connection:

import mlflow

# Test logging to verify connection
print(f"MLflow Tracking URI: {mlflow.get_tracking_uri()}")
with mlflow.start_run():
    print("✓ Successfully connected to MLflow!")

Next Steps

Now that your environment is connected to MLflow, try the other GenAI quickstarts:

• Instrument your app with tracing: Follow the quickstart to instrument your first GenAI app
• Evaluate your app's quality: Use the evaluation quickstart to systematically test and improve your app's quality