The ML Engineer’s Field Guide

Most ML interview prep is either too shallow (flashcards and trivia) or too theoretical (textbooks that prove theorems you will never write on a whiteboard).This book tries to sit in the middle: deep enough to defend at an ML bar-raiser, concrete enough to map every concept back to a production scenario, and written by someone who needed to actually use it.The author spent seventy-three days solving LeetCode in his head-during house chores, while swimming, while holding a sleeping baby-to pass the DSA rounds. Then he wrote this book (with heavy AI agents use) to pass the ML round. Every chapter exists because he needed to be able to explain the concept from first principles, in plain English, with a concrete example.Chapters and parts: - Foundations - training fundamentals, transformers, normalization, tokenization.- The LLM lifecycle - fine-tuning and adaptation, alignment, quantization.- Inference and serving - KV cache and speculative decoding, the serving stack.- Data - quality and curation, labels and annotation.- Applied systems - RAG architecture, AI agents and tool use.- Production engineering - MLOps and reliability, training debugging, safety.- The bigger picture - frameworks, multimodality, industry strategy.Each chapter opens with a production disaster. The apparatus that follows is motivated by the failure modes in that disaster, walked at three levels (plain-English, analogy, technical translation), and closed out with a trade-off matrix, a debugging playbook, and a callback that diagnoses the opening disaster using the chapter's own concepts. More than three hundred concepts in total, explained the way the author wishes someone had explained them to him.This book is not a textbook. Its a modern composite of stories, crawled and adapted to be usefull for other ML engineers.