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/tf-savedmodel.md

@@ -118,3 +118,80 @@ In this guide, we explored how to export Ultralytics YOLOv8 models to the TF Sav
 For further details on usage, visit the [TF SavedModel official documentation](https://www.tensorflow.org/guide/saved_model).
 
 For more information on integrating Ultralytics YOLOv8 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLOv8 in your projects.
+
+## FAQ
+
+### How do I export an Ultralytics YOLO model to TensorFlow SavedModel format?
+
+Exporting an Ultralytics YOLO model to the TensorFlow SavedModel format is straightforward. You can use either Python or CLI to achieve this:
+
+!!! Example "Exporting YOLOv8 to TF SavedModel"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load the YOLOv8 model
+        model = YOLO("yolov8n.pt")
+
+        # Export the model to TF SavedModel format
+        model.export(format="saved_model")  # creates '/yolov8n_saved_model'
+
+        # Load the exported TF SavedModel for inference
+        tf_savedmodel_model = YOLO("./yolov8n_saved_model")
+        results = tf_savedmodel_model("https://ultralytics.com/images/bus.jpg")
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Export the YOLOv8 model to TF SavedModel format
+        yolo export model=yolov8n.pt format=saved_model  # creates '/yolov8n_saved_model'
+
+        # Run inference with the exported model
+        yolo predict model='./yolov8n_saved_model' source='https://ultralytics.com/images/bus.jpg'
+        ```
+
+Refer to the [Ultralytics Export documentation](../modes/export.md) for more details.
+
+### Why should I use the TensorFlow SavedModel format?
+
+The TensorFlow SavedModel format offers several advantages for model deployment:
+
+- **Portability:** It provides a language-neutral format, making it easy to share and deploy models across different environments.
+- **Compatibility:** Integrates seamlessly with tools like TensorFlow Serving, TensorFlow Lite, and TensorFlow.js, which are essential for deploying models on various platforms, including web and mobile applications.
+- **Complete encapsulation:** Encodes the model architecture, weights, and compilation information, allowing for straightforward sharing and training continuation.
+
+For more benefits and deployment options, check out the [Ultralytics YOLO model deployment options](../guides/model-deployment-options.md).
+
+### What are the typical deployment scenarios for TF SavedModel?
+
+TF SavedModel can be deployed in various environments, including:
+
+- **TensorFlow Serving:** Ideal for production environments requiring scalable and high-performance model serving.
+- **Cloud Platforms:** Supports major cloud services like Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure for scalable model deployment.
+- **Mobile and Embedded Devices:** Using TensorFlow Lite to convert TF SavedModels allows for deployment on mobile devices, IoT devices, and microcontrollers.
+- **TensorFlow Runtime:** For C++ environments needing low-latency inference with better performance.
+
+For detailed deployment options, visit the official guides on [deploying TensorFlow models](https://www.tensorflow.org/tfx/guide/serving).
+
+### How can I install the necessary packages to export YOLOv8 models?
+
+To export YOLOv8 models, you need to install the `ultralytics` package. Run the following command in your terminal:
+
+```bash
+pip install ultralytics
+```
+
+For more detailed installation instructions and best practices, refer to our [Ultralytics Installation guide](../quickstart.md). If you encounter any issues, consult our [Common Issues guide](../guides/yolo-common-issues.md).
+
+### What are the key features of the TensorFlow SavedModel format?
+
+TF SavedModel format is beneficial for AI developers due to the following features:
+
+- **Portability:** Allows sharing and deployment across various environments effortlessly.
+- **Ease of Deployment:** Encapsulates the computational graph, trained parameters, and metadata into a single package, which simplifies loading and inference.
+- **Asset Management:** Supports external assets like vocabularies, ensuring they are available when the model loads.
+
+For further details, explore the [official TensorFlow documentation](https://www.tensorflow.org/guide/saved_model).