[DOCS] Removing Legacy Features from documentation

docs/articles_en/about-openvino/additional-resources/glossary.rst

@@ -38,7 +38,6 @@ Acronyms and Abbreviations
  LRN                 Local Response Normalization
  mAP                 Mean Average Precision
  Intel® OneDNN       Intel® OneAPI Deep Neural Network Library
- `mo`                Command-line tool for model conversion, CLI for ``tools.mo.convert_model`` (legacy)
  MVN                 Mean Variance Normalization
  NCDHW               Number of images, Channels, Depth, Height, Width
  NCHW                Number of images, Channels, Height, Width

docs/articles_en/about-openvino/compatibility-and-support/supported-devices.rst

@@ -31,11 +31,6 @@ OpenVINO offers the option of running automated inference with the following inf
 | :doc:`Automatic Batching <../../openvino-workflow/running-inference/inference-devices-and-modes/automatic-batching>`:
 | automatically groups inference requests to improve device utilization.
 
-| :doc:`(LEGACY) Multi-device Inference <./../../documentation/legacy-features/multi-device>`:
-| executes inference on multiple devices. Currently, this mode is considered a legacy
-  solution. Using Automatic Device Selection instead is advised.
-
-
 Feature Support and API Coverage
 #################################
 
@@ -52,7 +47,6 @@ Feature Support and API Coverage
  :doc:`Preprocessing acceleration <../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing>`                  Yes     Yes        No
  :doc:`Stateful models <../../openvino-workflow/running-inference/stateful-models>`                                                       Yes     Yes        Yes
  :doc:`Extensibility <../../documentation/openvino-extensibility>`                                                                        Yes     Yes        No
- :doc:`(LEGACY) Multi-device execution <./../../documentation/legacy-features/multi-device>`                                              Yes     Yes        Partial
 ======================================================================================================================================== ======= ========== ===========
 
 

docs/articles_en/about-openvino/performance-benchmarks/getting-performance-numbers.rst

@@ -103,7 +103,7 @@ General considerations
 
       Some image pre-processing can be baked into OpenVINO IR and accelerated accordingly.
       For more information, refer to
-      :doc:`Embedding Pre-processing <../../documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-embedding-preprocessing-computation>`
+      :doc:`Preprocessing API <../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing/preprocessing-api-details>`.
       and
       :doc:`General Runtime Optimizations <../../openvino-workflow/running-inference/optimize-inference/general-optimizations>`.
 
@@ -192,7 +192,7 @@ execution breakdown.
 
 For example, the table below is part of performance counters for
 :doc:`CPU inference <../../openvino-workflow/running-inference/inference-devices-and-modes/cpu-device>`.
-of a `TensorFlow implementation of ResNet-50 <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf>`__
+of a TensorFlow implementation of ResNet-50.
 Keep in mind that since the device is CPU, the ``realTime`` wall clock and the ``cpu`` time
 layers are the same. Information about layer precision is also stored in the performance
 counters.

docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst

@@ -15,13 +15,7 @@ Performance Information F.A.Q.
 
 .. dropdown:: Where can I find the models used in the performance benchmarks?
 
-   All models used are included in the GitHub repository of
-   :doc:`Open Model Zoo <../../documentation/legacy-features/model-zoo>`.
-
-   .. important::
-
-      Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
-      published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+   All models used are published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
 
 .. dropdown:: Will there be any new models added to the list used for benchmarking?
 
@@ -35,7 +29,7 @@ Performance Information F.A.Q.
    open-source tool within the Intel® Distribution of OpenVINO™ toolkit
    called :doc:`benchmark_app <../../learn-openvino/openvino-samples/benchmark-tool>`.
 
-   For diffusers (Stable-Diffusion) and foundational models (aka LLMs) please use the OpenVINO GenAI 
+   For diffusers (Stable-Diffusion) and foundational models (aka LLMs) please use the OpenVINO GenAI
    opensource repo `OpenVINO GenAI tools/llm_bench <https://github.com/openvinotoolkit/openvino.genai/tree/master/tools/llm_bench>`__
 
    For a simple instruction on testing performance, see the :doc:`Getting Performance Numbers Guide <getting-performance-numbers>`.
@@ -93,30 +87,6 @@ Performance Information F.A.Q.
         - BERT
         - question / answer
         - 128
-      * - `efficientdet-d0 <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/efficientdet-d0-tf>`__
-        - Efficientdet
-        - classification
-        - 512x512
-      * - `mask_rcnn_resnet50_atrous_coco <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mask_rcnn_resnet50_atrous_coco>`__
-        - Mask R-CNN ResNet 50 Atrous
-        - object instance segmentation
-        - 800x1365
-      * - `mobilenet-v2 <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-pytorch>`__
-        - Mobilenet V2 PyTorch
-        - classification
-        - 224x224
-      * - `resnet-50 <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf>`__
-        - ResNet-50_v1_ILSVRC-2012
-        - classification
-        - 224x224
-      * - `ssd-mobilenet-v1-coco <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_coco>`__
-        - ssd-mobilenet-V1-coco onnx model
-        - object detection
-        - 300x300
-      * - `ssd-resnet34-1200-onnx <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd-resnet34-1200-onnx>`__
-        - ssd-resnet34 onnx model
-        - object detection
-        - 1200x1200
       * - `yolov8n <https://github.com/ultralytics/ultralytics>`__
         - Yolov8nano
         - object detection

docs/articles_en/about-openvino/release-notes-openvino.rst

@@ -1668,7 +1668,7 @@ Deprecation And Support
 Using deprecated features and components is not advised. They are available to enable a smooth
 transition to new solutions and will be discontinued in the future. To keep using discontinued
 features, you will have to revert to the last LTS OpenVINO version supporting them.
-For more details, refer to the :doc:`OpenVINO Legacy Features and Components <../documentation/legacy-features>`
+For more details, refer to the `OpenVINO Legacy Features and Components <https://docs.openvino.ai/2024/documentation/legacy-features.html>__`
 page.
 
 Discontinued in 2024
@@ -1726,7 +1726,7 @@ Deprecated and to be removed in the future
 * Model Optimizer will be discontinued with OpenVINO 2025.0. Consider using the
   :doc:`new conversion methods <../openvino-workflow/model-preparation/convert-model-to-ir>`
   instead. For more details, see the
-  :doc:`model conversion transition guide <../documentation/legacy-features/transition-legacy-conversion-api>`.
+  `model conversion transition guide <https://docs.openvino.ai/2024/documentation/legacy-features/transition-legacy-conversion-api.html>`__.
 * OpenVINO property Affinity API will be discontinued with OpenVINO 2025.0.
   It will be replaced with CPU binding configurations (``ov::hint::enable_cpu_pinning``).
 * OpenVINO Model Server components:
@@ -1755,10 +1755,6 @@ Deprecated and to be removed in the future
 
        * See alternative: `Machine Translation Python* Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/machine_translation_demo/python/README.md>`__
 
-     * `Open Model Zoo Tools Tutorial <https://github.com/openvinotoolkit/openvino_notebooks/tree/2024.1/notebooks/model-tools>`__
-
-       * No alternatives, demonstrates deprecated tools.
-
      * `Super Resolution with OpenVINO™ <https://github.com/openvinotoolkit/openvino_notebooks/tree/2024.1/notebooks/vision-superresolution>`__
 
        * See alternative: `Super Resolution with PaddleGAN and OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/vision-paddlegan-superresolution>`__

docs/articles_en/about-openvino/release-notes-openvino/release-policy.rst

@@ -179,7 +179,7 @@ Additional Information
 * Binary distribution:
 
   * Download from `OpenVINO storage <https://storage.openvinotoolkit.org/repositories/openvino/packages/>`__
-  * `pypi.org <https://pypi.org/project/openvino-dev/>`__
+  * `pypi.org <https://pypi.org/project/openvino/>`__
   * `DockerHub* <https://hub.docker.com/u/openvino>`__
 
 

docs/articles_en/documentation.rst

@@ -13,7 +13,6 @@ Documentation
 
    API Reference <api/api_reference>
    OpenVINO IR format and Operation Sets <documentation/openvino-ir-format>
-   Legacy Features <documentation/legacy-features>
    Tool Ecosystem <documentation/openvino-ecosystem>
    OpenVINO Extensibility <documentation/openvino-extensibility>
    OpenVINO™ Security <documentation/openvino-security>