After instrumenting your training scripts to log experiments with the MLflow Tracking API, the next step is to visualize and analyze this information. While the API allows programmatic access, the MLflow User Interface (UI) provides a powerful web-based dashboard for interactive exploration, comparison, and management of your experiment runs.

Launching the MLflow UI

To start the UI, navigate to your project's root directory (or any directory containing the mlruns folder if you used the default local file store) in your terminal and run the command:

mlflow ui

By default, this command launches a local web server, typically accessible at http://127.0.0.1:5000. MLflow will print the exact URL in your terminal.

If you configured MLflow to use a different backend store (like a database or remote tracking server) when logging runs, you need to point the UI to the same location. Use the --backend-store-uri flag:

# Example for a PostgreSQL backend
mlflow ui --backend-store-uri postgresql://user:password@host:port/database

# Example for a remote tracking server
mlflow ui --backend-store-uri http://your-mlflow-server.com:5000

Open the provided URL in your web browser to access the interface.

Navigating the Interface

The MLflow UI presents a clean and organized view of your experiments and runs.

Experiments View

The default view usually lists all your experiments in the left-hand navigation panel. An experiment groups related runs, such as different attempts to train a model for a specific task. Clicking on an experiment name filters the main view to show only the runs associated with that experiment. The "Default" experiment is created automatically if you don't specify one when logging.

Runs Table

When an experiment is selected, the main area displays a table listing the individual runs. Each row represents a single execution of your tracked script (mlflow.start_run()). Key information is presented in columns:

Start Time: When the run began.
Duration: How long the run took.
Run Name: An optional, human-readable name (often auto-generated if not specified).
Source: The entry point script or notebook that initiated the run.
Version: The Git commit hash associated with the run (if run in a Git repository).
Parameters: Columns for each logged hyperparameter (e.g., learning_rate, n_estimators).
Metrics: Columns for each logged metric (e.g., accuracy, rmse, val_loss). You typically see the final logged value here.

You can customize the displayed columns, sort by any column (e.g., sort by accuracy descending to find the best run), and filter runs based on parameter values or metric scores using the search box.

Run Detail Page

Clicking on the start time or name of a specific run takes you to its detail page. This page provides a comprehensive view of everything logged for that single run:

Run ID: A unique identifier for the run.
Status: Indicates if the run finished, failed, or is still running.
Parameters: A dedicated section listing all logged hyperparameters and their values.
Metrics: A section showing logged metrics. For metrics logged multiple times (e.g., loss per epoch), MLflow often displays interactive plots showing the metric's history over steps or time.
Tags: Any custom tags associated with the run. Tags can be useful for adding qualitative information or labels.
Artifacts: A file browser view of all logged artifacts. This is where you'll find saved models, output files, images, plots, etc.

Exploring Run Details

The Run Detail page is where you examine the specifics of an individual experiment trial.

Parameters: Quickly verify the exact configuration used for a specific run.
Metrics: Analyze the performance results. If you logged metrics over training steps (e.g., logging validation accuracy after each epoch), you can visualize the training progress directly in the UI. Hovering over the plots often reveals exact values at specific points.
Artifacts: This is particularly useful for accessing the outputs of your run. You can:
- Download saved model files (e.g., .pkl, .h5, saved_model directory).
- View logged images or plots directly in the browser (e.g., confusion matrices, feature importance plots).
- Download any other files logged during the run, such as processed data samples or evaluation reports.

Comparing Experiment Runs

One of the most valuable features of the MLflow UI is its ability to compare multiple runs side-by-side.

Go back to the Runs Table view for an experiment.
Select the checkboxes next to the runs you want to compare.
Click the "Compare" button that appears.

This opens a dedicated comparison view:

Parameter Comparison: Shows the different parameter values across the selected runs, highlighting differences.
Metric Comparison: Displays the values of logged metrics for each run, making it easy to see which run performed best according to specific criteria.
Visualization Tools:
- Scatter Plot: Useful for visualizing the relationship between a parameter and a metric (e.g., learning_rate vs. accuracy) or between two metrics.
- Contour Plot: Helps visualize the relationship between two parameters and a metric.
- Parallel Coordinates Plot: Excellent for visualizing how combinations of multiple hyperparameters affect outcome metrics across many runs.

Scatter plot comparing Validation Accuracy against Learning Rate for five different experiment runs.

These comparison tools significantly accelerate the process of hyperparameter tuning and model selection by visually organizing the results of multiple trials.

Searching and Filtering

For experiments with many runs, the search functionality is indispensable. You can construct queries based on:

Metrics: metrics.accuracy > 0.9
Parameters: params.learning_rate = '0.01' or params.optimizer = 'Adam'
Tags: tags.data_version = 'v2'
Attributes: attributes.status = 'FINISHED'

Combine these using AND to create specific filters, allowing you to quickly find runs matching complex criteria.

The MLflow UI transforms experiment tracking from a passive logging activity into an active analysis and decision-making tool. By providing intuitive ways to view, sort, filter, compare, and deep-dive into individual runs, it helps you understand your model's behavior, identify optimal configurations, and ultimately build better machine learning models more efficiently.