# Track experiments using MLflow
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Amazon SageMaker Unified Studio supports two options for tracking experiments with MLflow: MLflow Apps and MLflow Tracking Servers. MLflow Apps are the latest offering with faster startup times and cross-account sharing, while MLflow Tracking Servers provide traditional MLflow functionality.

**Topics**
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## Use MLflow Apps for experiment tracking
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## Use MLflow Tracking Servers to track experiments
](#use-mlflow-tracking-servers)

## Use MLflow Apps for experiment tracking
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**Note**  
MLflow Apps are different from MLflow Tracking Servers. MLflow Apps offer additional features such as faster startup time and cross-account sharing. For information about connecting to existing MLflow Tracking Servers, see [Use MLflow Tracking Servers to track experiments](#use-mlflow-tracking-servers).

MLflow Apps are the latest managed MLflow offering in Amazon SageMaker Unified Studio and provide faster startup times, cross-account sharing, and integration with SageMaker AI features. MLflow Apps use MLflow 3.0 and support experiment tracking, model registry, and tracing for generative AI applications.

### Connect to an MLflow App
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You can connect to an existing MLflow App created in SageMaker AI Studio to track experiments and manage model versions. Note that you can't create a new MLflow App in Amazon SageMaker Unified Studio. You have to create this using SageMaker AI APIs or in SageMaker AI Studio.

To connect to an MLflow App, perform the following steps:

1. From your project's main page, choose **MLflow** from the left navigation menu.

1. Choose **Connect MLflow App**.

1. Enter an MLflow App Name

1. Provide a Connection name for identification

1. Enter the MLflow App ARN for your project

1. Choose **Connect to app**

### Manage MLflow Apps
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After you connect to an MLflow App, you can perform the following actions from the **MLflow** page:
+ Open MLflow – Choose the **Open** button next to the MLflow App to launch the MLflow UI and view experiments, models, and traces.
+ Edit – Update the connection with a new ARN.
+ Delete – Remove the connection to the MLflow App.

To access the **MLflow** page, choose **MLflow** from the left navigation menu.

## Use MLflow Tracking Servers to track experiments
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To get started, you should have an existing MLflow server created in SageMaker AI Studio. Make sure that you have the ARN to get started.

1. From your project's main page, choose **MLflow** from the left navigation menu.

1. Connect to an existing MLflow Tracking Server. Note that you can't create a new MLflow Server in Amazon SageMaker Unified Studio. You have to create this using SageMaker AI APIs or in SageMaker AI Studio.

1. Choose **Connect Tracking Server**

1. Enter a Tracking Server Name

1. Provide a Connection name for identification

1. Enter the MLflow Tracking Server ARN for your project

1. Choose **Connect to server**

1. Once connected, choose **Open MLflow** to launch the MLflow UI.

1. In the MLflow interface, view your experiments:
   + Experiments tab shows all tracked experiments
   + Models tab displays registered model versions
   + Prompts tab contains prompt templates and versions

1. You can perform additional actions such as
   + Stop ML Server
   + Use server to train model – this will launch a sample notebook which will provide instructions on how to use MLflow to train a linear regression model
   + Edit the connection with new ARNs
   + Delete the connection