Updating links in Model Optimization Guide (#13300)

Adding a link to Model Optimizer.

docs/optimization_guide/model_optimization_guide.md

@@ -12,13 +12,18 @@
 
 @endsphinxdirective
 
+
  Model optimization is an optional offline step of improving final model performance by applying special optimization methods, such as quantization, pruning, preprocessing optimization, etc. OpenVINO provides several tools to optimize models at different steps of model development:
 
-- **Model Optimizer** implements most of the optimization parameters to a model by default. Yet, you are free to configure mean/scale values, batch size, RGB vs BGR input channels, and other parameters to speed up preprocess of a model ([Embedding Preprocessing Computation](../MO_DG/prepare_model/Additional_Optimizations.md)).
+@sphinxdirective
 
-- **[Post-training Optimization w/ POT](../../tools/pot/docs/Introduction.md)** is designed to optimize inference of deep learning models by applying post-training methods that do not require model retraining or fine-tuning, for example, post-training 8-bit quantization.
+- :ref:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` implements most of the optimization parameters to a model by default. Yet, you are free to configure mean/scale values, batch size, RGB vs BGR input channels, and other parameters to speed up preprocess of a model (:ref:`Embedding Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>`).
 
-- **[Training-time Optimization w/ NNCF](./nncf/introduction.md)**, a suite of advanced methods for training-time model optimization within the DL framework, such as PyTorch and TensorFlow 2.x. It supports methods, like Quantization-aware Training and Filter Pruning. NNCF-optimized models can be inferred with OpenVINO using all the available workflows.
+- :ref:`Post-training Optimization w/ POT <pot_introduction>` is designed to optimize inference of deep learning models by applying post-training methods that do not require model retraining or fine-tuning, for example, post-training 8-bit quantization.
+
+- :ref:`Training-time Optimization w/ NNCF <tmo_introduction>`, a suite of advanced methods for training-time model optimization within the DL framework, such as PyTorch and TensorFlow 2.x. It supports methods, like Quantization-aware Training and Filter Pruning. NNCF-optimized models can be inferred with OpenVINO using all the available workflows.
+
+@endsphinxdirective
 
 
 ## Detailed workflow: