Systems for Machine Learning[D]

A computer engineering graduate with an embedded systems background, familiar with microcontrollers, computer architecture, and operating systems, inquired about the relevance of traditional computer engineering skills in ML engineering. They asked if knowledge of C, C++, Linux networking, memory management, multithreading, synchronization, and interrupts, as well as distributed systems, compiler optimizations (using LLVM), and parallel computing, would remain useful or become automated by AI in scaling ML systems.