Fix mkdocs.yml raw image URLs (#14213)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

docs/en/integrations/mlflow.md

@@ -117,3 +117,91 @@ yolo settings mlflow=False
 ## Conclusion
 
 MLflow logging integration with Ultralytics YOLO offers a streamlined way to keep track of your machine learning experiments. It empowers you to monitor performance metrics and manage artifacts effectively, thus aiding in robust model development and deployment. For further details please visit the MLflow [official documentation](https://mlflow.org/docs/latest/index.html).
+
+## FAQ
+
+### How do I set up MLflow logging with Ultralytics YOLO?
+
+To set up MLflow logging with Ultralytics YOLO, you first need to ensure MLflow is installed. You can install it using pip:
+
+```bash
+pip install mlflow
+```
+
+Next, enable MLflow logging in Ultralytics settings. This can be controlled using the `mlflow` key. For more information, see the [settings guide](../quickstart.md#ultralytics-settings).
+
+!!! Example "Update Ultralytics MLflow Settings"
+
+    === "Python"
+
+        ```python
+        from ultralytics import settings
+
+        # Update a setting
+        settings.update({"mlflow": True})
+
+        # Reset settings to default values
+        settings.reset()
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Update a setting
+        yolo settings runs_dir='/path/to/runs'
+
+        # Reset settings to default values
+        yolo settings reset
+        ```
+
+Finally, start a local MLflow server for tracking:
+
+```bash
+mlflow server --backend-store-uri runs/mlflow
+```
+
+### What metrics and parameters can I log using MLflow with Ultralytics YOLO?
+
+Ultralytics YOLO with MLflow supports logging various metrics, parameters, and artifacts throughout the training process:
+
+- **Metrics Logging**: Tracks metrics at the end of each epoch and upon training completion.
+- **Parameter Logging**: Logs all parameters used in the training process.
+- **Artifacts Logging**: Saves model artifacts like weights and configuration files after training.
+
+For more detailed information, visit the [Ultralytics YOLO tracking documentation](#features).
+
+### Can I disable MLflow logging once it is enabled?
+
+Yes, you can disable MLflow logging for Ultralytics YOLO by updating the settings. Here's how you can do it using the CLI:
+
+```bash
+yolo settings mlflow=False
+```
+
+For further customization and resetting settings, refer to the [settings guide](../quickstart.md#ultralytics-settings).
+
+### How can I start and stop an MLflow server for Ultralytics YOLO tracking?
+
+To start an MLflow server for tracking your experiments in Ultralytics YOLO, use the following command:
+
+```bash
+mlflow server --backend-store-uri runs/mlflow
+```
+
+This command starts a local server at http://127.0.0.1:5000 by default. If you need to stop running MLflow server instances, use the following bash command:
+
+```bash
+ps aux | grep 'mlflow' | grep -v 'grep' | awk '{print $2}' | xargs kill -9
+```
+
+Refer to the [commands section](#commands) for more command options.
+
+### What are the benefits of integrating MLflow with Ultralytics YOLO for experiment tracking?
+
+Integrating MLflow with Ultralytics YOLO offers several benefits for managing your machine learning experiments:
+
+- **Enhanced Experiment Tracking**: Easily track and compare different runs and their outcomes.
+- **Improved Model Reproducibility**: Ensure that your experiments are reproducible by logging all parameters and artifacts.
+- **Performance Monitoring**: Visualize performance metrics over time to make data-driven decisions for model improvements.
+
+For an in-depth look at setting up and leveraging MLflow with Ultralytics YOLO, explore the [MLflow Integration for Ultralytics YOLO](#introduction) documentation.