Optimize Python for Agentic AI

本课程是 Agentic AI Development & Security 专项课程的一部分

位教师：LearningMate

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2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Refactor Python code for AI agents to improve readability and performance. Apply coding standards and use profiling to optimize bottlenecks.

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授课语言：英语（English）

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该课程共有2个模块

"Optimize Python for Agentic AI" is an intermediate course for developers who want to elevate their Python code from functional to professional-grade. In the world of AI, inefficient or unreadable code can cripple an agent's performance and slow down team collaboration. This course equips you with the essential software engineering practices to write Python that is both highly efficient and exceptionally clear.

You will learn to apply clean-code conventions, including PEP 8 standards, type hints, and descriptive docstrings, to produce readable and maintainable modules that your teammates can easily understand and build upon. Through hands-on labs, you will master the art of performance tuning by systematically using profiling tools like cProfile to analyze runtime behavior, pinpoint hidden bottlenecks, and refactor code for significant speed improvements. By the end of this course, you will be able to confidently balance readability with runtime efficiency, ensuring the AI systems you build are not only intelligent but also robust, scalable, and production-ready.

You start by understanding how professional AI systems depend not only on smart logic but also on clean, readable Python. When deadlines hit, messy code slows teams down. This module shows you why clarity and consistency matter for maintainability and collaboration. You will refactor a memory manager from an AI agent, practice adding type hints and docstrings, and run style checks with flake8. You will see how these conventions keep complex AI projects manageable—and you will apply them to produce production-quality Python.

涵盖的内容

2个视频1篇阅读材料1个作业1个非评分实验室

Now that your code reads cleanly, it is time to make it run fast. Performance problems often hide in unexpected places. You will learn when to profile, how to interpret profiler data, and how to refactor Python for efficiency. By the end of this module, you will replace inefficient regex calls and confirm improvements through benchmarks. You will close the course by combining maintainability with performance in a final strategy report.