01Module 01 - Python Performance Engineering OverviewProfiling, Cython, Numba, memory optimisation, async performance, and Python at scale - turning Python code from slow to production-fast.02Profiling Python ApplicationscProfile, line_profiler, py-spy, memory_profiler, and Austin - finding real bottlenecks before optimising anything.03Cython and Native ExtensionsStatic typing in Python with Cython - turning Python bottlenecks into C-speed code without leaving the Python ecosystem.04Numba JIT CompilationLLVM-based JIT compilation for Python numerical code - GPU acceleration, parallel loops, and ufunc creation with @jit and @cuda.jit.05Python Memory OptimisationObject memory overhead, __slots__, generators, memory-mapped files, and GC tuning - reducing Python's memory footprint in production.06Async Performance Patternsasyncio internals, event loop tuning, connection pooling, backpressure, and high-throughput async patterns for production Python services.07Python at ScaleMultiprocessing, Ray, Dask, and distributed Python - moving beyond a single CPU core for data processing and model training.
