You're an Android developer. Every job posting now mentions "ML experience preferred." Your PM keeps asking about AI features. But every tutorial either drowns you in math or gives you copy-paste code you don't understand.
This book builds your mental models and engineer intuition — not memorization, not math. Every concept is explained through analogies you already understand as a developer, so you know why things work, not just how to copy them.
You'll build FinRisk, a complete credit risk app that runs a trained ML model entirely on-device. No server, no API calls, no internet required. The final models are 1.8 KB and 2.6 KB. Smaller than most app icons.
What's inside (134 pages, 6 chapters):
- How models learn: training loops, loss functions, gradients, and backpropagation — built on intuition, not equations
- End-to-end build: Python training, TFLite conversion, Android integration with Clean Architecture + Hilt + Compose
- Upgrade from logistic regression to a neural network — without changing a single line of Android code
- Production hardening: crash reporting, fallback classifiers, kill switches, model monitoring, drift detection
For Android developers (2+ years) who want to add ML features without becoming data scientists. Not for LLMs/ChatGPT — this is on-device inference, models that run locally with zero network dependency.
Complete source code available at github.com/vsay01/FinRisk
