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Crack ML System Design Interviews Like a Pro — Part II
Netflix Real-World Case Study
In the first part of this blog post, I talked about the framework for handling ML Interviews. Now let’s dive into what many consider the most valuable part of ML interview prep — real-world case studies. I’ve carefully selected this example because it represents the problems you’re likely to encounter both in interviews and on the job. This case study follows our design framework from the last post, but with specific implementation details that make these systems work in production.
So without delay, picture this scenario: You’re in an interview at Netflix, and they ask you to design their content recommendation system. This isn’t just a theoretical exercise — Netflix estimates that their recommendation system saves them $1B+ yearly in customer retention. Let’s break this down step by step, using our framework from the last post.
Requirements Gathering
First, let’s nail down those requirements. Remember — don’t jump into solutions yet! Here are maybe some of the key questions you should/could ask so that the interviewer knows that you are thinking in the right direction and if not it could help them guide you in the right direction:
