Before we start running sample TensorFlow iOS and Android apps, let's clarify one big picture. TensorFlow currently has two approaches to developing and deploying deep learning apps on mobile devices: TensorFlow Mobile and TensorFlow Lite. TensorFlow Mobile was part of TensorFlow from the beginning, and TensorFlow Lite is a newer way to develop and deploy TensorFlow apps, as it offers better performance and smaller app size. But there's one key factor that will let us focus on TensorFlow Mobile in this book, while still covering TensorFlow Lite in one chapter: TensorFlow Lite is still in developer preview as of TensorFlow 1.8 and Google I/O 2018 in May 2018. So to develop production-ready mobile TensorFlow apps now, you have to use TensorFlow Mobile, as recommended by Google.
Another reason we decided to focus on TensorFlow Mobile now is while TensorFlow Lite only offers a limited support for model operators, TensorFlow Mobile supports customization to add new operators not supported by TensorFlow Mobile by default, which you'll see happens pretty often in our various models of different AI apps.
But in the future, when TensorFlow Lite is out of developer preview, it's likely to replace TensorFlow Mobile, or at least overcome its current limitations. To get yourself ready for that, we'll cover TensorFlow Lite in detail in a later chapter.
