I think a curriculum like this is neat and might help with interviews since you go wide and have a checklist of things that you need to learn.
I'm on a totally different path for learning inference engineering. I self-host a voice AI app that has ~2000 monthly active users on my own GPU box.
This forces me to learn about production serving, KV cache, quantization, inference engine, observability and economics, prefill optimization since I'm optimizing for TTFT instead of decode speed, and many more.
It's fun since every optimization you do directly translate to a better user experience or allow you to serve more users using the same hardware.
I'm on a totally different path for learning inference engineering. I self-host a voice AI app that has ~2000 monthly active users on my own GPU box.
This forces me to learn about production serving, KV cache, quantization, inference engine, observability and economics, prefill optimization since I'm optimizing for TTFT instead of decode speed, and many more.
It's fun since every optimization you do directly translate to a better user experience or allow you to serve more users using the same hardware.