A good starting point for this book would be to get our hands dirty playing with the ML model dataset and training the model. It will be useful to jump in quickly in further chapters. We are not going to deal with basic ML algorithms here; instead, this will be more of a practical-based approach. You can download the complete code base from our GitHub repository (https://github.com/intrepidkarthi/MLmobileapps).
Throughout this book, we will deal with two frameworks: TensorFlow Lite and Core ML. These two frameworks are tightly coupled with Android and iOS. We will look into the basics of ML on a mobile device with TensorFlow Lite. It is assumed that the reader knows the basics of TensorFlow and basic ML algorithms, because this book is not going to cover those elements.
As previously stated, every one of us is holding a smartphone in our pocket almost all of the time. We have a rich amount of data that comes from the sensors available on these devices. As well as this, we have data that is coming from edge devices. At the time of writing this book, there are close to 23 billion devices under this category, including smart speakers, smart watches, and smart sensors. High-end technologies that used to only be available on costlier devices are now available on cheaper devices as well. This exponential rate of growth for these devices paves the way to ML on these devices.
While there are many reasons to run ML on the devices, the foremost reason is that of latency. If you are processing video or audio, you don't want to keep pinging the server with data to and fro. Another advantage is that you can do the processing when the device is in offline mode. Importantly, the data stays on the device itself, local to the user. This is more energy-efficient in terms of battery/power consumption.
While this looks like an advantage, there are also a few cons to this approach. Most of our devices are running on batteries with limited capacity, less processing capability, and strict memory constraints. The TensorFlow framework won't resolve all these issues, which is why it has shifted to have a framework that works efficiently under all these conditions. TensorFlow Lite is a lightweight, energy-and memory-efficient framework that will run on embedded smaller-form factor devices.
