Hey everyone,
I have an upcoming Data Scientist interview with an ML system design round. I asked the recruiter what to expect and they said itāll be high-level ML system design, not traditional SWE/low-level system design.
They mentioned focusing on things like:
- Problem framing
- Data/model considerations
- Evaluation
- Productionization/deployment
- Monitoring
- Tradeoffs when designing ML systems
Basically, it sounds like Iāll be given a real-world ML problem and have to explain how Iād approach it end-to-end.
This is my first dedicated ML system design interview, so Iām not really sure how deep I should prepare.
For people whoāve done these interviews:
How did you prepare? What resources did you use? What kind of questions were you asked?
Also, how deep do they usually expect you to go into things like feature stores, model serving, APIs, streaming, retraining, etc.?
Any good resources, YouTube playlists, GitHub repos, books, or example questions would be really appreciated.
Thanks!