Refactor and Test LLM Microservices

本课程是 Microservices Architecture for AI Systems 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。

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3 小时完成

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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

3 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Apply TDD and systematic refactoring to build and maintain robust, production-quality LLM-powered microservices.

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作业

2 任务¹

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授课语言：英语（English）

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该课程共有2个模块

As AI applications are built at record speed, many teams are accumulating significant "technical debt," leading to brittle, unpredictable, and expensive systems. "Refactor and Test LLM Microservices" is an intermediate course designed for software developers and ML engineers who want to build production-grade AI applications that last. This course moves beyond notebooks and scripts to instill the software engineering discipline required for robust microservices.

You will master Test-Driven Development (TDD), learning to write failing unit tests before implementing new API endpoints to ensure correctness from the start. You will also learn to act on code-review feedback by systematically refactoring complex code, breaking down monolithic functions into clean, readable, and maintainable modules. Through hands-on labs in a VS Code environment, you will refactor a legacy service and build a new, fully tested API endpoint, ensuring your work is not just functional, but also scalable and reliable.

This module introduces the discipline of Test-Driven Development (TDD) as a crucial practice for building reliable software. You will explore how a test-first approach prevents costly production failures, what the TDD cycle entails, and how to apply it to add a new, fully tested endpoint to an LLM-powered microservice.

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

This module focuses on improving the long-term health of a codebase. You will explore why refactoring is essential for managing technical debt and how to methodically improve code by acting on realistic, expert-level feedback. You will also gain hands-on experience in breaking down complex functions to improve readability and maintainability, ultimately transforming professional critique into higher-quality code.