Machine Learning Systems Engineering Questions

Design and optimization of machine learning systems at the system level. Topics include efficient implementation of linear algebra and matrix operations, numerical stability, batching and vectorization strategies, memory management, and hardware and resource considerations. Covers feature computation pipelines and feature store patterns, online and offline feature computation, caching strategies, and data locality. Also includes large scale data processing patterns such as streaming and batch processing, parallelization and distributed computation tradeoffs, profiling and benchmarking, and techniques to reduce end to end latency and total resource cost.