ML Systems Reading Group

I regularly read papers about ML and systems which takes quite some time. For my own learning I think it is good to summarize main ideas of a paper after reading it and compare it with other papers. If you want a digest of recent papers on topics related to various ML workloads you came to the right place.

I aim to maintain a wide range of subject coverage, including all aspects of ML workloads (such as data processing, distributed training, model serving, and hyperparameter tuning), at any scale (be it Large Language Models with billions of parameters or few-kilobyte TinyML), and across all layers (including scheduling, communication, hardware-aware optimizations, etc).