Excited about these books. 3rd edition of HOML is a beast, 834 pages; Aurélion Géron is my hero. It will be good to have as a new baseline in this fast-moving field.

DMLS is new to me - looking forward to strengthening my data engineering / ML engineering chops.

What I've been up to lately: the deep reinforcement learning AI for Umpire is still in the works---the self-play games take a long time when playing from the model rather than the random baseline, just due to inference time. So the datagen is the bottleneck right now. And, that datagen is surprisingly CPU-bound, even with the PyTorch code handling all of the inference. So, I just run it in multiple processes, though this is data-hungry. Unfortunately, due to a quirk of libtorch, even multithreading it would result in multiple copies of the model residing in memory. (Actually, we deal with that anyway, thanks to the use of async.)

And so, with my own spare cycles, in addition to the books, I'm tackling a new ML task, which shall remain nameless for the time being. Let's just say I'll be hanging out in Label Studio for the next while!