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[DOCS] Deploy and run documentation sections (#17708)

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* first draft

* change name

* restructure

* workflow headers change

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* remove deployment guide

* change deployment description
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docs/Documentation/deployment_guide_introduction.md
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# Running and Deploying Inference {#openvino_docs_deployment_guide_introduction}
@sphinxdirective
.. toctree::
:maxdepth: 1
:hidden:
Run and Deploy Locally <openvino_deployment_guide>
Deploy via Model Serving <ovms_what_is_openvino_model_server>
Once you have a model that meets both OpenVINO™ and your requirements, you can choose how to deploy it with your application.
.. panels::
:doc:`Deploy via OpenVINO Runtime <openvino_deployment_guide>`
^^^^^^^^^^^^^^
Local deployment uses OpenVINO Runtime that is called from, and linked to, the application directly.
It utilizes resources available to the system and provides the quickest way of launching inference.
---
:doc:`Deploy via Model Server <ovms_what_is_openvino_model_server>`
^^^^^^^^^^^^^^
Deployment via OpenVINO Model Server allows the application to connect to the inference server set up remotely.
This way inference can use external resources instead of those available to the application itself.
Apart from the default deployment options, you may also :doc:`deploy your application for the TensorFlow framework with OpenVINO Integration <ovtf_integration>`
@endsphinxdirective

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docs/Documentation/openvino_workflow.md
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Model Preparation <openvino_docs_model_processing_introduction>
Model Optimization and Compression <openvino_docs_model_optimization_guide>
Running and Deploying Inference <openvino_docs_deployment_guide_introduction>
Running Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide>
Deployment on a Local System <openvino_deployment_guide>
Deployment on a Model Server <ovms_what_is_openvino_model_server>
| :doc:`Model Preparation <openvino_docs_model_processing_introduction>`
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| :doc:`Model Optimization and Compression <openvino_docs_model_optimization_guide>`
| In this section you will find out how to optimize a model to achieve better inference performance. It describes multiple optimization methods for both the training and post-training stages.
| :doc:`Deployment <openvino_docs_deployment_guide_introduction>`
| This section explains the process of deploying your own inference application using either OpenVINO Runtime or OpenVINO Model Server. It describes how to run inference which is the most basic form of deployment and the quickest way of launching inference.
| :doc:`Running Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
| This section explains describes how to run inference which is the most basic form of deployment and the quickest way of launching inference.
Once you have a model that meets both OpenVINO™ and your requirements, you can choose how to deploy it with your application.
| :doc:`Option 1. Deployment via OpenVINO Runtime <openvino_deployment_guide>`
| Local deployment uses OpenVINO Runtime that is called from, and linked to, the application directly.
| It utilizes resources available to the system and provides the quickest way of launching inference.
| Deployment on a local system requires performing the steps from the running inference section.
| :doc:`Option 2. Deployment via Model Server <ovms_what_is_openvino_model_server>`
| Deployment via OpenVINO Model Server allows the application to connect to the inference server set up remotely.
| This way inference can use external resources instead of those available to the application itself.
| Deployment on a model server can be done quickly and without performing any additional steps described in the running inference section.
@endsphinxdirective

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docs/OV_Runtime_UG/deployment/deployment_intro.md
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# Run and Deploy Locally {#openvino_deployment_guide}
# Deploy Locally {#openvino_deployment_guide}
@sphinxdirective
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:maxdepth: 1
:hidden:
Run Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide>
Optimize Inference <openvino_docs_deployment_optimization_guide_dldt_optimization_guide>
Deploy Application with Deployment Manager <openvino_docs_install_guides_deployment_manager_tool>
Local Distribution Libraries <openvino_docs_deploy_local_distribution>

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openvino_docs_OV_UG_ShapeInference
openvino_docs_OV_UG_DynamicShapes
openvino_docs_OV_UG_model_state_intro
Optimize Inference <openvino_docs_deployment_optimization_guide_dldt_optimization_guide>
OpenVINO Runtime is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the OpenVINO Runtime API to read an Intermediate Representation (IR), TensorFlow, TensorFlow Lite, ONNX, or PaddlePaddle model and execute it on preferred devices.