Removed obsolete documentation (#10504)

* Removed obsolete documentation

* Fixed documentation

* Additional fix

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md

@@ -10,8 +10,8 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 1. [Configure the Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md) for Caffe\*.
 2. [Convert a Caffe\* Model](#Convert_From_Caffe) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values
-3. Test the model in the Intermediate Representation format using the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md)
-4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in your application to deploy the model in the target environment
+3. Test the model in the Intermediate Representation format using the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md)
+4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in your application to deploy the model in the target environment
 
 ## Supported Topologies
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md

@@ -16,8 +16,8 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 1. [Configure the Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md) for Kaldi\*.
 2. [Convert a Kaldi\* Model](#Convert_From_Kaldi) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
-3. Test the model in the Intermediate Representation format using the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
-4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in your application to deploy the model in the target environment.
+3. Test the model in the Intermediate Representation format using the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
+4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in your application to deploy the model in the target environment.
 
 > **NOTE**: The Model Optimizer supports the [nnet1](http://kaldi-asr.org/doc/dnn1.html) and [nnet2](http://kaldi-asr.org/doc/dnn2.html) formats of Kaldi models. Support of the [nnet3](http://kaldi-asr.org/doc/dnn3.html) format is limited.
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -17,8 +17,8 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 1. [Configure the Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md) for MXNet* (MXNet was used to train your model)
 2. [Convert a MXNet model](#ConvertMxNet) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values
-3. Test the model in the Intermediate Representation format using the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md)
-4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in your application to deploy the model in the target environment
+3. Test the model in the Intermediate Representation format using the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md)
+4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in your application to deploy the model in the target environment
 
 ## Supported Topologies
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Paddle.md

@@ -4,8 +4,8 @@ A summary of the steps for optimizing and deploying a model trained with Paddle\
 
 1. [Configure the Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md) for Paddle\*.
 2. [Convert a Paddle\* Model](#Convert_From_Paddle) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases.
-3. Test the model in the Intermediate Representation format using the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
-4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in your application to deploy the model in the target environment.
+3. Test the model in the Intermediate Representation format using the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in the target environment via provided Inference Engine [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
+4. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in your application to deploy the model in the target environment.
 
 ## Supported Topologies
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md

@@ -48,7 +48,7 @@ PyTorch* framework is supported through export to ONNX\* format. A summary of th
 1. [Configure the Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md) for ONNX\*.
 2. [Export PyTorch model to ONNX\*](#export-to-onnx).
 3. [Convert an ONNX\* model](Convert_Model_From_ONNX.md) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
-4. Test the model in the Intermediate Representation format using the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
+4. Test the model in the Intermediate Representation format using the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in the target environment via provided [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
 5. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the Inference Engine in your application to deploy the model in the target environment.
 
 ## Export PyTorch\* Model to ONNX\* Format <a name="export-to-onnx"></a>

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -31,7 +31,7 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 1. [Configure the Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md) for TensorFlow\* (TensorFlow was used to train your model).
 2. [Freeze the TensorFlow model](#freeze-the-tensorflow-model) if your model is not already frozen or skip this step and use the [instruction](#loading-nonfrozen-models) to a convert a non-frozen model.
 3. [Convert a TensorFlow\* model](#Convert_From_TF) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
-4. Test the model in the Intermediate Representation format using the [Inference Engine](../../../OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
+4. Test the model in the Intermediate Representation format using the [OpenVINO™ Runtime](../../../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) in the target environment via provided [sample applications](../../../OV_Runtime_UG/Samples_Overview.md).
 5. [Integrate](../../../OV_Runtime_UG/Samples_Overview.md) the Inference Engine in your application to deploy the model in the target environment.
 
 ## Supported Topologies