Ai Ml Mental Model For Sdes

PART 1 — AI / ML Mental Model for SDEs

As an SDE, think of Machine Learning as a compilation process: * Traditional Software: Source Code + Compiler = Binary executable. * Machine Learning: Data + Training Algorithm = Model weights (the "Binary").

Once trained, the model is a frozen artifact—a massive matrix of numbers. * Inference: Running the model. It is essentially a pure, stateless function f(input) -> output, though in practice we use caching (state) to optimize it. * Parameters: The variables (floats) that make up the model. A "70B model" means the binary consists of 70 billion floating-point numbers.

SDE Takeaway: You treat the model as a black-box microservice. Your job in HLD is optimizing the I/O, memory allocation, caching, and batching around this extremely heavy, compute-intensive black box.

---