Machine Learning Engineering_Andriy Burkov.pdf

“In theory, there is no difference between theory and practice. But in practice, there is.” —Benjamin Brewster “The perfect project plan is possible if one rst documents a list of all the unknowns.” —Bill Langley “When you're fundraising, it's AI. When you're hiring, it's ML. When you're implementing, it's linear regression. When you're debugging, it's printf().” —Baron Schwartz The book is distributed on the “read rst, buy later” principle. Andriy Burkov<> Machine Learning Engineering - Draft Foreword Foreword byCassie Kozyrkov, Chief Decision Scientist at Google, author of the course Making Friends with Machine Learning on Google Cloud Platform I'd like to let you in on a secret: when people say machine learning it sounds like there's only one discipline here. Surprise! There are actually two machine learnings, and they are as dierent as innovating in food recipes and inventing new kitchen appliances. Both are noble callings, as long as you don't get them confused; imagine hiring a pastry chef to build you an oven or an electrical engineer to bake bread for you! The bad news is that almost everyone does mix these two machine learnings up. No wonder so many businesses fail at machine learning as a result. What no one seems to tell beginners is that most machine learning courses and textbooks are about Machine Learning Research  how to build ovens (and microwaves, blenders, toasters, kettles. . . the kitchen sink!) from scratch, not how to cook things and innovate with recipes at enormous scale. In other words, if you're looking for opportunities to create innovative ML-based solutions to business problems, you want the discipline called Applied Machine Learning, not Machine Learning Research, so most books won't suit your needs. And now for the good news! You're looking at one of the few true Applied Machine Learning books out there. That's right, you found one! A real applied needle in the haystack of research-oriented stu. Excellent job, dear reader. . .