[DOCS] Port Frontend Extensions and OTX page (#16135)

* [DOCS] Add OTX page to Ecosystem  (#16118)

* add otx page

* change ecosystem page

* add ote img

* move ote page to rst

* fix path

* add path

* img test

* otx page

* add docs to ecosystem page

* [DOCS] Fix Frontend Extensions snippets (#16120)

* move fe to rst

* fix code snippets

* add more line breaks

* fix tabsets

* fix link

* fix anchor

* test

* fixing link

* change tab directive

* fix tabs

* align code tabs

* fix link

* fix snippets

* add dlwb to ecosystem

* change ecosystem menu

* exclude fe page

docs/Documentation/openvino_ecosystem.md

@@ -6,8 +6,8 @@
    :maxdepth: 1
    :hidden:
 
-   ovtf_integration
    ote_documentation
+   ovtf_integration
    ovsa_get_started
    openvino_inference_engine_tools_compile_tool_README
    openvino_docs_tuning_utilities
@@ -27,6 +27,15 @@ More resources:
 * [GitHub](https://github.com/openvinotoolkit/nncf)
 * [PyPI](https://pypi.org/project/nncf/)
 
+### OpenVINO™ Training Extensions
+A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
+
+More resources:
+
+* [Overview](@ref ote_documentation)
+* [GitHub](https://github.com/openvinotoolkit/training_extensions)
+* [Documentation](https://openvinotoolkit.github.io/training_extensions/stable/guide/get_started/introduction.html)
+
 ### OpenVINO™ Security Add-on	 
 A solution for Model Developers and Independent Software Vendors to use secure packaging and secure model execution.	 
 
@@ -50,6 +59,7 @@ More resources:
 * [documentation on GitHub](https://dlstreamer.github.io/index.html)
 * [installation Guide on GitHub](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Install-Guide)
 
+
 ### DL Workbench
 A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyze, optimize, and build your pre-trained models. You can do all that by visiting [Intel® DevCloud for the Edge](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench on-line.
 
@@ -58,12 +68,6 @@ More resources:
 * [Docker Hub](https://hub.docker.com/r/openvino/workbench)
 * [PyPI](https://pypi.org/project/openvino-workbench/)
 
-### OpenVINO™ Training Extensions (OTE)
-A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
-
-More resources:
-* [GitHub](https://github.com/openvinotoolkit/training_extensions)
-
 ### Computer Vision Annotation Tool (CVAT)
 An online, interactive video and image annotation tool for computer vision purposes.
 

docs/Documentation/openvino_training_extensions.md

@@ -0,0 +1,40 @@
+# OpenVINO™ Training Extensions {#ote_documentation}
+
+@sphinxdirective 
+
+OpenVINO™ Training Extensions provide a suite of advanced algorithms to train
+Deep Learning models and convert them using the `OpenVINO™
+toolkit <https://software.intel.com/en-us/openvino-toolkit>`__ for optimized
+inference. It allows you to export and convert the models to the needed format. OpenVINO Training Extensions independently create and train the model. It is open-sourced and available on `GitHub <https://github.com/openvinotoolkit/training_extensions>`__. Read the OpenVINO Training Extensions `documentation <https://openvinotoolkit.github.io/training_extensions/stable/guide/get_started/introduction.html>`__ to learn more.
+
+Detailed Workflow
+#################
+
+.. image:: ./_static/images/training_extensions_framework.png
+
+1. To start working with OpenVINO Training Extensions, prepare and annotate your dataset. For example, on CVAT.
+
+2. OpenVINO Training Extensions train the model, using training interface, and evaluate the model quality on your dataset, using evaluation and inference interfaces.
+
+   .. note:: 
+      Prepare a separate dataset or split the dataset you have for more accurate quality evaluation.
+
+3. Having successful evaluation results received, you have an opportunity to deploy your model or continue optimizing it, using NNCF and POT. For more information about these frameworks, go to :doc:`Optimization Guide <openvino_docs_model_optimization_guide>`.
+
+If the results are unsatisfactory, add datasets and perform the same steps, starting with dataset annotation.
+
+OpenVINO Training Extensions Components
+#######################################
+
+- `OpenVINO Training Extensions SDK <https://github.com/openvinotoolkit/training_extensions/tree/master/ote_sdk>`__
+- `OpenVINO Training Extensions CLI <https://github.com/openvinotoolkit/training_extensions/tree/master/ote_cli>`__
+- `OpenVINO Training Extensions Algorithms <https://github.com/openvinotoolkit/training_extensions/tree/master/external>`__
+
+Tutorials
+#########
+
+`Object Detection <https://github.com/openvinotoolkit/training_extensions/blob/master/ote_cli/notebooks/train.ipynb>`__
+
+@endsphinxdirective 
+
+

docs/_static/images/training_extensions_framework.png

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