A small technical journal around machine learning systems, privacy, deployment, performance engineering, and the gap between research ideas and working software.
Federated learning and differential privacy introduce constraints around data movement, aggregation, evaluation, and reproducibility. The interesting engineering begins where those constraints meet the pipeline.
A model that works in a notebook is only the beginning. Reproducible preprocessing, stable inference, validation, packaging, and observability are what turn an experiment into a service.
Memory behavior is shaped by cache levels, access patterns, bandwidth, and the distance between the CPU and data. A useful benchmark makes those effects measurable without pretending one number explains the entire system.