[DOCS][Keras] Document Keras 3 models support and OpenVINO backend (#…

…28345)

**Details:** Document Keras 3 models support and OpenVINO backend

**Ticket:** TBD

---------

Signed-off-by: Kazantsev, Roman <[email protected]>
Co-authored-by: Karol Blaszczak <[email protected]>

README.md

@@ -127,6 +127,7 @@ Learn how to run LLMs and GenAI with [Samples](https://github.com/openvinotoolki
 -   [OpenVINO Execution Provider for ONNX Runtime](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html) - use OpenVINO as a backend with your existing ONNX Runtime code.
 -   [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/openvino/) - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO.
 -   [LangChain](https://python.langchain.com/docs/integrations/llms/openvino/) - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications.
+-   [Keras 3](https://github.com/keras-team/keras) - Keras 3 is a multi-backend deep learning framework. Users can switch model inference to the OpenVINO backend using the Keras API.
 
 Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!
 

docs/articles_en/openvino-workflow/model-preparation/convert-model-keras.rst

@@ -0,0 +1,87 @@
+Converting a Keras Model
+========================
+
+
+.. meta::
+   :description: Learn how to convert a model from the
+                 Keras format to the OpenVINO Model.
+
+
+This document explains the process of converting Keras 3 models to the OpenVINO Intermediate Representation (IR) format.
+For instructions on converting Keras 2 models, refer to :doc:`TensorFlow Model Conversion <convert-model-tensorflow>`.
+
+To convert a Keras 3 model, first export it to a lightweight TensorFlow SavedModel artifact,
+and then convert it to an OpenVINO model, using the ``convert_model`` function.
+Here is a code example of how to do this:
+
+.. code-block:: py
+   :force:
+
+   import keras_hub
+   import openvino as ov
+
+   model = keras_hub.models.BertTextClassifier.from_preset(
+       "bert_base_en_uncased",
+       num_classes=4,
+       preprocessor=None,
+   )
+
+   # export to SavedModel
+   model.export("bert_base")
+
+   # convert to OpenVINO model
+   ov_model = ov.convert_model("bert_base")
+
+
+.. note::
+
+   The resulting OpenVINO IR model can be saved to drive with no additional, Keras-specific steps.
+   Use the standard ``ov.save_model(ov_model,'model.xml')`` command. 
+
+Alternatively, a model exported to TensorFlow SavedModel format can also be converted to OpenVINO IR using the ``ovc`` tool. Here is an example:
+
+.. code-block:: sh
+   :force:
+
+   ovc bert_base
+
+
+Run inference in Keras 3 with the OpenVINO backend
+##################################################
+
+Starting with release 3.8, Keras provides native integration with the OpenVINO backend for accelerated inference.
+This integration enables you to leverage OpenVINO performance optimizations directly within the Keras workflow, enabling faster inference on OpenVINO supported hardware.
+
+To switch to the OpenVINO backend in Keras 3, set the ``KERAS_BACKEND`` environment variable to ``"openvino"``
+or specify the backend in the local configuration file at ``~/.keras/keras.json``.
+Here is an example of how to infer a model (trained with PyTorch, JAX, or TensorFlow backends) in Keras 3, using the OpenVINO backend:
+
+.. code-block:: py
+   :force:
+
+   import os
+
+   os.environ["KERAS_BACKEND"] = "openvino"
+   import numpy as np
+   import keras
+   import keras_hub
+
+   features = {
+       "token_ids": np.ones(shape=(2, 12), dtype="int32"),
+       "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
+       "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
+   }
+
+   # take a model from KerasHub
+   bert = keras_hub.models.BertTextClassifier.from_preset(
+       "bert_base_en_uncased",
+       num_classes=4,
+       preprocessor=None,
+   )
+
+   predictions = bert.predict(features)
+
+.. note::
+
+   The OpenVINO backend may currently lack support for some operations.
+   This will be addressed in upcoming Keras releases as operation coverage is being expanded.

docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst

@@ -14,6 +14,7 @@ Convert to OpenVINO IR
    Convert from TensorFlow Lite <convert-model-tensorflow-lite>
    Convert from PaddlePaddle <convert-model-paddle>
    Convert from JAX/Flax <convert-model-jax>
+   Convert from Keras <convert-model-keras>