AI Systems & Python Performance Fundamentals

Vectorization, AsyncIO, memory layouts, and hardware-aware programming.

2 weeks · wk 1–2Review pace5 topics15 Interview Questions

Why this pace:Python is the glue language of AI, but native Python is slow. Production AI systems rely on C/C++ extensions, GPU kernels, vectorized memory buffers, and asynchronous network streams.

SEC FOCUS

Avoid unbuffered in-memory loads of multi-gigabyte datasets; stream datasets or memory-map them to prevent out-of-memory kernel panics.

Topics & Action Items (5)

NumPy Internals: Vectorization, Strides & Memory Layout

4-5 hrs

AsyncIO & High-Concurrency I/O in Python

4 hrs

Memory Management, GIL & Rust/C FFI Bindings

5 hrs

GPU Hardware Architecture: CUDA Cores, Tensor Cores & HBM

6 hrs

Profiling, Tracing & Memory Diagnostics (PyTorch Profiler)

3 hrs

Next Stage

Deep Learning & Neural Foundation Mechanics