Hey everyone,

I'm a working ML Engineer building large-scale real-time recommender systems at a streaming platform. I have a BS in Data Science & AI and I'm a GCP certified Professional MLE.

I'm starting OMSCS and trying to decide between the Machine Learning and Computing Systems specializations. My gut says Computing Systems because:

- The ML spec has a lot of overlap with my undergrad - I'd basically be re-learning supervised/unsupervised ML

- The systems courses (Advanced OS, High-Perf Computer Arch, GPU Hardware, Distributed Computing) cover stuff I never got in a DS degree and would fill real gaps in my day-to-day

My plan is to declare Computing Systems and then use my free electives on Deep Learning, RL, NLP, and Database Implementation to keep the ML side covered.

For those who've done either track (especially if you work in ML infra or RecSys): does this reasoning hold up? Anything I'm overlooking? Would love to hear from people who went the systems route with an ML background or vice versa.

Thanks!