#Mobile Artificial Intelligence Projects
A book I co-authored, published by Packt in 2019. Seven hands-on projects that build real AI apps for Android and iOS, on the device.
I wrote Mobile Artificial Intelligence Projects with Arun Padmanabhan and Matt R. Cole. It is not a theory book. It is seven projects, and in each one you build and ship a working AI feature to a phone using TensorFlow Lite, Core ML, and PyTorch. Real-estate price prediction, recognising handwritten digits, predicting car damage from a photo, sentiment analysis, and more, all running on-device rather than calling a server. It is the second of my two mobile-AI books, following Machine Learning Projects for Mobile Applications, which is tighter on the model-to-device deployment path.
The reason it is structured as projects and not lectures is the same reason I still write about building things here: you do not learn machine learning by reading about it. You learn it by shipping something small and being wrong and fixing it. I argued that case at more length in why you should read fewer ML books, not more — advice I am comfortable giving about my own book.
## What it covers
The seven projects walk through the core of applied mobile AI: computer vision, natural language processing, neural networks, and deep learning, each grounded in an app you can actually run. It is written for engineers who already know how to code and want to put AI on a mobile platform, on both Android and iOS, without a cloud dependency in the loop.
The complete source for every project is on Packt's GitHub: PacktPublishing/Mobile-Artificial-Intelligence-Projects.
## Where the ideas went next
The book was 2019. On-device AI has moved a long way since, and I have kept shipping it. DailyVox is a privacy-first on-device iOS app built on the same principle the book is about: the intelligence runs on your phone, not on my server. If you want the modern version of what the book teaches, that thread continues in the on-device app architecture behind DailyVox, on-device AI memory, and the economics of on-device AI.