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tensorflow/lite/g3doc/android/play_services.md

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+# TensorFlow Lite in Google Play services
+
+TensorFlow Lite is available in Google Play services runtime for all Android
+devices running the current version of Play services. This runtime allows you to
+run machine learning (ML) models without statically bundling TensorFlow Lite
+libraries into your app.
+
+With the Google Play services API, you can reduce the size of your apps and gain
+improved performance from the latest stable version of the libraries. TensorFlow
+Lite in Google Play services is the recommended way to use TensorFlow Lite on
+Android.
+
+You can get started with the Play services runtime with the
+[Quickstart](../android/quickstart), which provides a step-by-step guide to
+implement a sample application. If you are already using stand-alone TensorFlow
+Lite in your app, refer to the
+[Migrating from stand-alone TensorFlow Lite](#migrating) section to update an
+existing app to use the Play services runtime. For more information about Google
+Play services, see the
+[Google Play services](https://developers.google.com/android/guides/overview)
+website.
+
+<aside class="note"> <b>Terms:</b> By accessing or using TensorFlow Lite in
+Google Play services APIs, you agree to the <a href="#tos">Terms of Service</a>.
+Please read and understand all applicable terms and policies before accessing
+the APIs. </aside>
+
+## Using the Play services runtime
+
+The TensorFlow Lite in Google Play services is available through the following
+programming language apis:
+
+-   Java API - [see guide](../android/java)
+-   C API - [see guide](../android/native)
+
+## Limitations
+
+TensorFlow Lite in Google Play services has the following limitations:
+
+*   Support for hardware acceleration delegates is limited to the delegates
+    listed in the [Hardware acceleration](#hardware-acceleration) section. No
+    other acceleration delegates are supported.
+*   Experimental or deprecated TensorFlow Lite APIs, including custom ops, are
+    not supported.
+
+## Support and feedback {:#support}
+
+You can provide feedback and get support through the TensorFlow Issue Tracker.
+Please report issues and support requests using the
+[Issue template](https://github.com/tensorflow/tensorflow/issues/new?title=TensorFlow+Lite+in+Play+Services+issue&template=tflite-in-play-services.md)
+for TensorFlow Lite in Google Play services.
+
+## Terms of service {:#tos}
+
+Use of TensorFlow Lite in Google Play services APIs is subject to the
+[Google APIs Terms of Service](https://developers.google.com/terms/).
+
+### Privacy and data collection
+
+When you use TensorFlow Lite in Google Play services APIs, processing of the
+input data, such as images, video, text, fully happens on-device, and TensorFlow
+Lite in Google Play services APIs does not send that data to Google servers. As
+a result, you can use our APIs for processing data that should not leave the
+device.
+
+The TensorFlow Lite in Google Play services APIs may contact Google servers from
+time to time in order to receive things like bug fixes, updated models and
+hardware accelerator compatibility information. The TensorFlow Lite in Google
+Play services APIs also sends metrics about the performance and utilization of
+the APIs in your app to Google. Google uses this metrics data to measure
+performance, debug, maintain and improve the APIs, and detect misuse or abuse,
+as further described in our
+[Privacy Policy](https://policies.google.com/privacy).
+
+**You are responsible for informing users of your app about Google's processing
+of TensorFlow Lite in Google Play services APIs metrics data as required by
+applicable law.**
+
+Data we collect includes the following:
+
++   Device information (such as manufacturer, model, OS version and build) and
+    available ML hardware accelerators (GPU and DSP). Used for diagnostics and
+    usage analytics.
++   Device identifier used for diagnostics and usage analytics.
++   App information (package name, app version). Used for diagnostics and usage
+    analytics.
++   API configuration (such as which delegates are being used). Used for
+    diagnostics and usage analytics.
++   Event type (such as interpreter creation, inference). Used for diagnostics
+    and usage analytics.
++   Error codes. Used for diagnostics.
++   Performance metrics. Used for diagnostics.
+
+## Next steps
+
+For more information about implementing machine learning in your mobile
+application with TensorFlow Lite, see the
+[TensorFlow Lite Developer Guide](https://www.tensorflow.org/lite/guide). You
+can find additional TensorFlow Lite models for image classification, object
+detection, and other applications on the
+[TensorFlow Hub](https://tfhub.dev/s?deployment-format=lite).