Add homepage for TFLite Model Maker under "guide"

PiperOrigin-RevId: 325368667
Change-Id: I3f4d1162684564373a9abbda59c520928448a0cf

tensorflow/lite/g3doc/_book.yaml

@@ -93,6 +93,11 @@ upper_tabs:
       - title: "1.x compatibility"
         path: /lite/convert/1x_compatibility
 
+      - heading: "Create a model"
+      - title: "TensorFlow Lite Model Maker"
+        status: experimental
+        path: /lite/guide/model_maker
+
       - heading: "Inference"
       - title: "Overview"
         path: /lite/guide/inference

tensorflow/lite/g3doc/guide/get_started.md

@@ -67,6 +67,10 @@ If you have designed and trained your own TensorFlow model, or you have trained
 a model obtained from another source, you must
 [convert it to the TensorFlow Lite format](#2_convert_the_model_format).
 
+You can also try [The TensorFlow Lite Model Maker library](model_maker.md) which
+simplifies the process of training a TensorFlow Lite model using custom
+datasets.
+
 ## 2. Convert the model
 
 <a id="2_convert_the_model_format"></a>

tensorflow/lite/g3doc/guide/model_maker.md

@@ -0,0 +1,60 @@
+# TensorFlow Lite Model Maker
+
+## Overview
+
+The TensorFlow Lite Model Maker library simplifies the process of training a
+TensorFlow Lite model using custom dataset. It uses transfer learning to reduce
+the amount of training data required and shorten the training time.
+
+## Supported Tasks
+
+The Model Maker library currently supports the following ML tasks. Click the
+links below for guides on how to train the model.
+
+Supported Tasks                                                                                          | Task Utility
+-------------------------------------------------------------------------------------------------------- | ------------
+Image Classification [guide](https://www.tensorflow.org/lite/tutorials/model_maker_image_classification) | Classify images into predefined categories.
+Text Classification [guide](https://www.tensorflow.org/lite/tutorials/model_maker_text_classification)   | Classify text into predefined categories.
+Question Answer [guide](https://www.tensorflow.org/lite/tutorials/model_maker_question_answer)           | Find the answer in a certain context for a given question.
+
+## End-to-End Example
+
+Model Maker allows you to train a TensorFlow Lite model using custom datasets in
+just a few lines of code. For example, here are the steps to train an image
+classification model.
+
+```python
+# Load input data specific to an on-device ML app.
+data = ImageClassifierDataLoader.from_folder('flower_photos/')
+train_data, test_data = data.split(0.9)
+
+# Customize the TensorFlow model.
+model = image_classifier.create(data)
+
+# Evaluate the model.
+loss, accuracy = model.evaluate(test_data)
+
+# Export to Tensorflow Lite model and label file in `export_dir`.
+model.export(export_dir='/tmp/')
+```
+
+For more details, see the
+[image classification guide](https://www.tensorflow.org/lite/tutorials/model_maker_image_classification).
+
+## Installation
+
+There are two ways to install Model Maker.
+
+*   Install a prebuilt pip package
+
+```shell
+pip install tflite-model-maker
+```
+
+*   Clone the source code from GitHub and install.
+
+```shell
+git clone https://github.com/tensorflow/examples
+cd examples
+pip install .[model_maker]
+```