In r/datascience, data scientists compare notes on being screened with ML system design at scale and converge on prep via ML system design books, structured frameworks, and LeetCode style practice, while debating whether such interviews fit DS roles.
This round was taken by a Staff DS and mostly consisted of ML Design at scale.
A standard answer to ML system design includes: * Problem definition, scope, and requirements * High-level design * Data preparation and analysis * Model training (often coupled with the previous step) * Deployment * Post-deployment
I recommend reading the ML System Design book by Alex Xu.
Medium leetcode
This is a pretty dumb interview for Warner Bros Discovery given that they pay low salaries even for DS.
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