Any good comprehensive resources targeted towards ML design interview rounds? Looking for something similar to Grokking/donnamartin like refs. There are so many common applications that are being asked like designing recommendation systems (products, dates, jobs), ranking/relevance models(news feed, search), matching problems(matching dates, driver-rider, gamer match) etc but am surprised by lack of resources online. Please help if you know any. Thanks
Honestly I guess grokking would be good for that. A system doesn’t drastically change just because it is ML based. What’s happening at MZ that makes you consider a change.
Blind is cloud heavy OP, unfortunately you wont get good advice for this. What is your specialization in ML?
one of the comprehensive materials out there https://github.com/rjurney/Agile_Data_Code_2 , and just search on youtube "machine learning system design"
If it’s an ML role, then you need to know traditional ML algorithm and when to use them and how to build an infrastructure for training / evaluation / tracking analytics etc. If it’s for an ML infrastructure role, then you should expect regular system design interviews.
Yes. Any resources online where multiple approaches are presented and trade offs are discussed? Company blogs seems to be a good place to start but they may be too specific given their problem. Ideally looking for an overview of different design components involved, different ways to think about designing them, pros/cons etc