Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/integrations/onnx.md

@@ -6,21 +6,21 @@ keywords: YOLOv8, ONNX, model export, Ultralytics, ONNX Runtime, machine learnin
 
 # ONNX Export for YOLOv8 Models
 
-Often, when deploying computer vision models, you'll need a model format that's both flexible and compatible with multiple platforms.
+Often, when deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, you'll need a model format that's both flexible and compatible with multiple platforms.
 
 Exporting [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to ONNX format streamlines deployment and ensures optimal performance across various environments. This guide will show you how to easily convert your YOLOv8 models to ONNX and enhance their scalability and effectiveness in real-world applications.
 
 ## ONNX and ONNX Runtime
 
-[ONNX](https://onnx.ai/), which stands for Open Neural Network Exchange, is a community project that Facebook and Microsoft initially developed. The ongoing development of ONNX is a collaborative effort supported by various organizations like IBM, Amazon (through AWS), and Google. The project aims to create an open file format designed to represent machine learning models in a way that allows them to be used across different AI frameworks and hardware.
+[ONNX](https://onnx.ai/), which stands for Open [Neural Network](https://www.ultralytics.com/glossary/neural-network-nn) Exchange, is a community project that Facebook and Microsoft initially developed. The ongoing development of ONNX is a collaborative effort supported by various organizations like IBM, Amazon (through AWS), and Google. The project aims to create an open file format designed to represent [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models in a way that allows them to be used across different AI frameworks and hardware.
 
-ONNX models can be used to transition between different frameworks seamlessly. For instance, a deep learning model trained in PyTorch can be exported to ONNX format and then easily imported into TensorFlow.
+ONNX models can be used to transition between different frameworks seamlessly. For instance, a [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) model trained in PyTorch can be exported to ONNX format and then easily imported into TensorFlow.
 
 <p align="center">
   <img width="100%" src="https://www.aurigait.com/wp-content/uploads/2023/01/1_unnamed.png" alt="ONNX">
 </p>
 
-Alternatively, ONNX models can be used with ONNX Runtime. [ONNX Runtime](https://onnxruntime.ai/) is a versatile cross-platform accelerator for machine learning models that is compatible with frameworks like PyTorch, TensorFlow, TFLite, scikit-learn, etc.
+Alternatively, ONNX models can be used with ONNX Runtime. [ONNX Runtime](https://onnxruntime.ai/) is a versatile cross-platform accelerator for machine learning models that is compatible with frameworks like PyTorch, [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), TFLite, scikit-learn, etc.
 
 ONNX Runtime optimizes the execution of ONNX models by leveraging hardware-specific capabilities. This optimization allows the models to run efficiently and with high performance on various hardware platforms, including CPUs, GPUs, and specialized accelerators.
 
@@ -28,7 +28,7 @@ ONNX Runtime optimizes the execution of ONNX models by leveraging hardware-speci
   <img width="100%" src="https://www.aurigait.com/wp-content/uploads/2023/01/unnamed-1.png" alt="ONNX with ONNX Runtime">
 </p>
 
-Whether used independently or in tandem with ONNX Runtime, ONNX provides a flexible solution for machine learning model deployment and compatibility.
+Whether used independently or in tandem with ONNX Runtime, ONNX provides a flexible solution for machine learning [model deployment](https://www.ultralytics.com/glossary/model-deployment) and compatibility.
 
 ## Key Features of ONNX Models
 
@@ -177,7 +177,7 @@ Using ONNX Runtime for deploying YOLOv8 models offers several advantages:
 
 - **Cross-platform compatibility**: ONNX Runtime supports various platforms, such as Windows, macOS, and Linux, ensuring your models run smoothly across different environments.
 - **Hardware acceleration**: ONNX Runtime can leverage hardware-specific optimizations for CPUs, GPUs, and dedicated accelerators, providing high-performance inference.
-- **Framework interoperability**: Models trained in popular frameworks like PyTorch or TensorFlow can be easily converted to ONNX format and run using ONNX Runtime.
+- **Framework interoperability**: Models trained in popular frameworks like [PyTorch](https://www.ultralytics.com/glossary/pytorch) or TensorFlow can be easily converted to ONNX format and run using ONNX Runtime.
 
 Learn more by checking the [ONNX Runtime documentation](https://onnxruntime.ai/docs/api/python/api_summary.html).