August 2025
Intermediate to advanced
460 pages
9h 55m
English
In the age of big data, artificial intelligence, and intensive data processing, an essential characteristic of well written Python programs is their ability to efficiently manage both memory usage and runtime performance. As part of the Software Development Life Cycle (SDLC), it is common practice to profile our programs, anticipate performance limitations and strive for efficiency in both aspects.
In addition to utilizing traditional tools for profiling program performance and addressing inefficiencies, we can leverage the model mastery of Large Language Models (LLMs) to code profile snippets, analyze maximal capacities, and optimize code to handle larger workloads.
In this chapter, we will examine ...
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