Peng Shao published a book on machine learning interviews with 151 questions

Peng Shao released the book "ML Interview. 151 Questions from FAANG," dedicated to preparing for hiring in ML. This is not a collection of dry problems, but a step-by-step breakdown of how to pass interviews — from basic theory to system design and discussion of production infrastructure.

What's Inside the Book

The main idea of the book is to gather in one place the questions candidates actually face in machine learning interviews. Shao covers the entire preparation route: mathematical foundations, key ML concepts, programming, data work, model evaluation, and common mistakes in answers. Based on the description, the emphasis is placed not only on what you need to know, but also on how you answer: how to structure your thinking, what to clarify with the interviewer, and where not to venture into unnecessary theory.

An additional value of this format is that ML interviews rarely limit themselves to algorithmic questions. Candidates are usually expected to understand the full lifecycle of a model: how data is prepared, how metrics are chosen, how offline evaluation differs from real deployment, and why even a strong model can fail in production. The book promises to bridge this gap between textbook theory and the expectations of companies hiring engineers and applied specialists for real tasks.

How Preparation is Structured

According to the description, the book breaks down not only the content of questions, but also the logic of the hiring process itself. This is important because the same knowledge is tested differently in phone screening, technical interviews, and deep project discussions. Sometimes a short and precise answer in a minute is needed, sometimes — full argumentation with trade-offs, constraints, and business context. For candidates, this is often harder than the formulas themselves: you need to quickly understand what level of detail is expected right now.

At the heart of the book is a set of recurring blocks that almost always come up in the hiring process. This is convenient for preparation: a candidate can skip reading the material linearly and quickly identify weak spots and work through them separately. This format is especially useful when there's little time before an interview and you need a compact checklist of topics to refresh. At the same time, you can see how the depth of questions changes from stage to stage.

• basic concepts of machine learning and programming
• strategies for answering frequent questions and analyzing typical mistakes
• transition from phone screening to deep technical interview
• designing ML systems and discussing infrastructure

This approach makes the book useful not only as a textbook but also as a simulator before a specific hiring process. Instead of scattered notes on theory, problems, and system design, a candidate gets a unified preparation route. This is especially useful for those who haven't been on the market in a long time and underestimate how broad ML interviews have become: today they check not only knowledge of models, but also engineering thinking, product priorities, and the ability to explain solutions in clear language.

Who Will Benefit from the Book

The material is positioned as universal: it will suit both beginners who are just building their foundation and experienced specialists who need a quick overview before a series of interviews. For the former, the book can serve as a map of topics to avoid drowning in an endless list of algorithms, libraries, and papers. For the latter — a way to check blind spots: for example, system design, infrastructure, metric selection, or argumentation around trade-offs between model quality, cost, and speed.

Against the backdrop of growing ML vacancies and increasing requirements, such guides become more practical than classical machine learning textbooks. They don't replace deep theory, but help answer a more practical question: what exactly to review before an interview, in what order, and how to translate knowledge into clear, confident answers. For Russian-speaking audiences, this is especially useful if you need guidance on the international interview format and expectations of global teams.

What It Means

Peng Shao's book shows that the ML hiring market requires not only knowledge of models, but also readiness to discuss code, metrics, infrastructure, and business trade-offs as a unified system.