How to prepare for ML system design interview as a data scientist?

Hello,

I need some advice on the following topic/adjacent. I got rejected from Warner Bros Discovery as a Data Scientist in my 2nd round.

This round was taken by a Staff DS and mostly consisted of ML Design at scale. Basically, kind of how the model needs to be deployed and designed for a large scale.

Since my work is mostly around analytics and traditional ML, I have never worked at that large scale (mostly ~50K SKU, 10K outlets, ~100K transactions etc) I was also not sure, as I assumed the MLops/DevOps teams handled such things. The only large scale data I handled was for static analysis.

After the interview, I got to research a bit on the topic and I got to know of the book Designing Machine Learning Systems by Chip Huyen (If only I had it earlier :( ).

I would really like some advice on how to get knowledgeable on this topic without going too deep. Basically, how much is too much?

Thanks a lot!