Removed obsolete documentation (#10504)

* Removed obsolete documentation

* Fixed documentation

* Additional fix

tools/pot/docs/LowPrecisionOptimizationGuide.md

@@ -39,8 +39,7 @@ The diagram below shows the optimization flow for the new model with OpenVINO an
 
 ![](images/low_precision_flow.png)
 
-- **Step 0: Model enabling**. In this step we should ensure that the model trained on the target dataset can be successfully inferred with OpenVINO 
-[Inference Engine](@ref openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide) in floating-point precision.
+- **Step 0: Model enabling**. In this step we should ensure that the model trained on the target dataset can be successfully inferred with [OpenVINO™ Runtime](@ref openvino_docs_OV_Runtime_User_Guide) in floating-point precision.
 This process involves use of [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide) tool to convert the model from the source framework 
 to the OpenVINO Intermediate Representation (IR) and run it on CPU with Inference Engine. 
   > **NOTE**: This step presumes that the model has the same accuracy as in the original training framework and enabled in the [Accuracy Checker](@ref omz_tools_accuracy_checker) tool or through the custom validation sample.
@@ -49,4 +48,4 @@ Post-training quantization is also really fast and usually takes several minutes
 For more information on best practices of post-training optimization please refer to the [Post-training Optimization Best practices](BestPractices.md).
 - **Step2: Quantization-Aware Training**: If the accuracy of the quantized model does not satisfy accuracy criteria, there is step two which implies QAT using [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) for [PyTorch*](https://pytorch.org/) and [TensorFlow*](https://www.tensorflow.org/) models.
 At this step, we assume the user has an original training pipeline of the model written on TensorFlow or PyTorch and NNCF is integrated into it.
-After this step, you can get an accurate optimized model that can be converted to OpenVINO Intermediate Representation (IR) using Model Optimizer component and inferred with OpenVINO Inference Engine.
+After this step, you can get an accurate optimized model that can be converted to OpenVINO Intermediate Representation (IR) using Model Optimizer component and inferred with OpenVINO Inference Engine.

tools/pot/openvino/tools/pot/api/README.md

@@ -52,7 +52,7 @@ The POT Python* API for model optimization can be used in the following cases:
 - [Accuracy Checker](@ref omz_tools_accuracy_checker) tool does not support the model or dataset.
 - POT does not support the model in the [Simplified Mode](@ref pot_docs_BestPractices) or produces the optimized model with low 
 accuracy in this mode.
-- You already have the Python* script to validate the accuracy of the model using the [OpenVINO™ Inference Engine](@ref openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide).  
+- You already have the Python* script to validate the accuracy of the model using the [OpenVINO™ Runtime](@ref openvino_docs_OV_Runtime_User_Guide).  
 
 ## API Description
 
@@ -362,7 +362,7 @@ Before running the optimization tool it's highly recommended to make sure that
 
 As was described above, `DataLoader`, `Metric` and `Engine` interfaces should be implemented in order to create 
 the custom optimization pipeline for your model. There might be a case you have the Python* validation script for your 
-model using the [OpenVINO™ Inference Engine](@ref openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide),
+model using the [OpenVINO™ Runtime](@ref openvino_docs_OV_Runtime_User_Guide),
 which in practice includes loading a dataset, model inference, and calculating the accuracy metric.
 So you just need to wrap the existing functions of your validation script in `DataLoader`, `Metric` and `Engine` interfaces. 
 In another case, you need to implement interfaces from scratch.