Machine Learning System Design Interview

Aminian, Ali,Xu, Alex

ISBN 10: 1736049127 / ISBN 13: 9781736049129

Editore: ByeByteGo, 2023

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Good reading copy of a PLEASE NOTE: Ex-Library edition with the usual stamps and stickers. Officially withdrawn from the library and stamped "no longer property of library," purchased at a charity even for the library system Otherwise, a clean text -- NO writing, NO highlighting to text. A useful reading copy. Oversized. Clean text -- NO writing, NO highlighting to text.ÂPLEASE NOTE: Domestic US media (standard) US orders ONLY. NO international orders. Codice articolo mon0000218529

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Titolo: Machine Learning System Design Interview

Casa editrice: ByeByteGo

Data di pubblicazione: 2023

Legatura: paperback

Condizione: Good

Descrizione articolo

Riassunto:

Machine learning system design interviews are the most difficult to tackle of all technical interview questions. This book provides a reliable strategy and knowledge base for approaching a broad range of ML system design questions. It provides a step-by-step framework for tackling an ML system design question. It includes many real-world examples to illustrate the systematic approach, with detailed steps you can follow.

This book is an essential resource for anyone interested in ML system design, whether they are beginners or experienced engineers. Meanwhile, if you need to prepare for an ML interview, this book is specifically written for you.

What’s inside?
- An insider’s take on what interviewers really look for and why.
- A 7-step framework for solving any ML system design interview question.
- 10 real ML system design interview questions with detailed solutions.
- 211 diagrams that visually explain how various systems work.

Table Of Contents
Chapter 1 Introduction and Overview
Chapter 2 Visual Search System
Chapter 3 Google Street View Blurring System
Chapter 4 YouTube Video Search
Chapter 5 Harmful Content Detection
Chapter 6 Video Recommendation System
Chapter 7 Event Recommendation System
Chapter 8 Ad Click Prediction on Social Platforms
Chapter 9 Similar Listings on Vacation Rental Platforms
Chapter 10 Personalized News Feed
Chapter 11 People You May Know