[{"_1":2,"_68":-5,"_69":-5},"loaderData",{"_3":4,"_38":39,"_66":-5,"_67":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","light","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"needsLocaleCookieSync",false,"routes/_store+/_readers+/email_author.$slug/route",{"_40":41,"_65":-5},"book",{"_9":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzczMzc","title","Machine Learning for Android Engineers: From Theory to On-Device Inference","state","published","contactAuthorMethod","email","aboutText","

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

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