The Anatomy of a Machine Learning System Design Interview Question

The Anatomy of a Machine Learning System Design Interview Question

Today we’ll prepare you for your next ML system design interview by breaking down how they’re unique, what you should do to prepare, and the five steps you should use to solve any ML problem.

Written by Educative Co-Founder, Fahim ul Haq.

Machine learning system design interviews have become increasingly common as more industries adopt ML systems. While similar in some ways to generic system design interviews, ML interviews are different enough to trip up even the most seasoned developers. The most common problem is to get stuck or intimidated by the large scale of most ML solutions.

Today we’ll prepare you for your next ML system design interview by breaking down how they’re unique, what you should do to prepare, and the five steps you should use to solve any ML problem.

Today we’ll cover:

  • What’s different about the ML system design interview
  • How to prepare for an ML system design interview
  • Five steps to solve any ML system design problem
  • Wrapping up and resources

What’s Different About the ML System Design Interview

The general setup of a machine learning system design interview is similar to a generic SDI. For both, you’ll be placed with an interviewer for 45 to 60 minutes and be asked to think through the components of a program.

ML interviews generally focus more on the macro-level (like architecture, recommendation systems, and scaling) and avoid deeper design discussions on topics like availability and reliability.

In ML interviews, you’ll be asked high-level questions on how you’d set up each component of a system (data gathering, ML algorithm, signals) to handle a heavy workload and still adapt quickly.

For the machine learning SDI, you’ll be expected to explain how your program acquires data and achieves scalability.

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