@@ -14,14 +14,16 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 
 # Getting Started Samples
 
-|AI Tools preset | Component      | Folder                                             | Description
-|--------------------------| --------- | ------------------------------------------------ | -
-|Deep Learning| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-PyTorch](INC-Quantization-Sample-for-PyTorch)                     | Performs INT8 quantization on a Hugging Face BERT model.
-|Deep Learning| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-Tensorflow](INC-Sample-for-Tensorflow)                     | Quantizes a FP32 model into INT8 by Intel® Neural Compressor (INC) and compares the performance between FP32 and INT8.
-|Deep Learning| Intel® Optimization for TensorFlow* | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted)               | A simple training example for TensorFlow.
-|Deep Learning|Intel® Extension of PyTorch | [Getting Started with Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md) | A simple training example for Intel® Extension of PyTorch.
-|Classical Machine Learning| Scikit-learn (OneDAL) | [Intel_Extension_For_SKLearn_GettingStarted](Intel_Extension_For_SKLearn_GettingStarted) | Speed up a scikit-learn application using Intel oneDAL.
-|Deep Learning|oneCCL Bindings for PyTorch | [Intel oneCCL Bindings For PyTorch GettingStarted](Intel_oneCCL_Bindings_For_PyTorch_GettingStarted)         | Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU. |
-|Deep Learning|JAX Getting Started Sample | [IntelJAX GettingStarted](https://github.com/oneapi-src/oneAPI-samples/tree/development/AI-and-Analytics/Getting-Started-Samples/IntelJAX_GettingStarted)  | The JAX Getting Started sample demonstrates how to train a JAX model and run inference on Intel® hardware. |
-
-*Other names and brands may be claimed as the property of others. [Trademarks](https://www.intel.com/content/www/us/en/legal/trademarks.html)
+| AI Tools preset            | Component                      | Folder                                                                                   | Description
+| -------------------------- | ------------------------------ | ---------------------------------------------------------------------------------------- | -------------------------------------------------------
+| Classical Machine Learning | Scikit-learn (OneDAL)          | [Intel_Extension_For_SKLearn_GettingStarted](Intel_Extension_For_SKLearn_GettingStarted) | Speed up a scikit-learn application using Intel oneDAL.
+| Deep Learning              | Intel® Extension of PyTorch    | [Getting Started with Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/README.md) | A simple training example for Intel® Extension of PyTorch.
+| Deep Learning              | Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-PyTorch](INC-Quantization-Sample-for-PyTorch) | Performs INT8 quantization on a Hugging Face BERT model.
+| Deep Learning              | Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-Tensorflow](INC-Sample-for-Tensorflow)        | Quantizes a FP32 model into INT8 by Intel® Neural Compressor (INC) and compares the performance between FP32 and INT8.
+| Deep Learning              | JAX Getting Started Sample     | [IntelJAX GettingStarted](IntelJAX_GettingStarted)                                       | The JAX Getting Started sample demonstrates how to train a JAX model and run inference on Intel® hardware.
+| Deep Learning              | oneCCL Bindings for PyTorch    | [Intel oneCCL Bindings For PyTorch GettingStarted](Intel_oneCCL_Bindings_For_PyTorch_GettingStarted) | Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU.
+| Deep Learning              | ONNX Runtime*                  | [Quickstart Examples for PyTorch, TensorFlow, and SciKit Learn](https://onnxruntime.ai/docs/get-started/with-python.html#quickstart-examples-for-pytorch-tensorflow-and-scikit-learn) | Train a model using your favorite framework, export to ONNX format and inference in any supported ONNX Runtime language.
+| Deep Learning              | TensorFlow*                    | [TensorFlow_GettingStarted](TensorFlow_GettingStarted)                                   | A simple training example for TensorFlow.
+
+*Other names and brands may be claimed as the property of others.
+[Trademarks](https://www.intel.com/content/www/us/en/legal/trademarks.html)