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/index.md

@@ -112,3 +112,27 @@ By writing a guide or tutorial, you can help expand our documentation and provid
 To contribute, please check out our [Contributing Guide](../help/contributing.md) for instructions on how to submit a Pull Request (PR) 🛠️. We eagerly await your contributions!
 
 Let's collaborate to make the Ultralytics YOLO ecosystem more expansive and feature-rich 🙏!
+
+## FAQ
+
+### What is Ultralytics HUB, and how does it streamline the ML workflow?
+
+Ultralytics HUB is a cloud-based platform designed to make machine learning (ML) workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLOv8 models without needing extensive coding skills. You can explore the key features on the [Ultralytics HUB](https://hub.ultralytics.com) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
+
+### How do I integrate Ultralytics YOLO models with Roboflow for dataset management?
+
+Integrating Ultralytics YOLO models with Roboflow enhances dataset management by providing robust tools for annotation, preprocessing, and augmentation. To get started, follow the steps on the [Roboflow](roboflow.md) integration page. This partnership ensures efficient dataset handling, which is crucial for developing accurate and robust YOLO models.
+
+### Can I track the performance of my Ultralytics models using MLFlow?
+
+Yes, you can. Integrating MLFlow with Ultralytics models allows you to track experiments, improve reproducibility, and streamline the entire ML lifecycle. Detailed instructions for setting up this integration can be found on the [MLFlow](mlflow.md) integration page. This integration is particularly useful for monitoring model metrics and managing the ML workflow efficiently.
+
+### What are the benefits of using Neural Magic for YOLOv8 model optimization?
+
+Neural Magic optimizes YOLOv8 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models that perform better on resource-limited hardware. Check out the [Neural Magic](neural-magic.md) integration page to learn how to implement these optimizations for superior performance and leaner models. This is especially beneficial for deployment on edge devices.
+
+### How do I deploy Ultralytics YOLO models with Gradio for interactive demos?
+
+To deploy Ultralytics YOLO models with Gradio for interactive object detection demos, you can follow the steps outlined on the [Gradio](gradio.md) integration page. Gradio allows you to create easy-to-use web interfaces for real-time model inference, making it an excellent tool for showcasing your YOLO model's capabilities in a user-friendly format suitable for both developers and end-users.
+
+By addressing these common questions, we aim to improve user experience and provide valuable insights into the powerful capabilities of Ultralytics products. Incorporating these FAQs will not only enhance the documentation but also drive more organic traffic to the Ultralytics website.