Analyze & Deploy Scalable LLM Architectures

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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3个模块

深入了解一个主题并学习基础知识。

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3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

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作业

6 任务¹

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授课语言：英语（English）

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该课程共有3个模块

Analyze & Deploy Scalable LLM Architectures is an intermediate course for ML engineers and AI practitioners tasked with moving large language model (LLM) prototypes into production. Many powerful models fail under real-world load due to architectural flaws. This course teaches you to prevent that.

You will learn to analyze multi-stage architectures such as RAG to diagnose and quantify performance bottlenecks with evidence, not assumptions. You will then master the tools of production-grade operations, designing and writing declarative Helm charts to deploy containerized LLM applications on Kubernetes. The curriculum focuses on building resilient, scalable systems by implementing Horizontal Pod Autoscaling (HPA) to handle unpredictable traffic and managing the full deployment lifecycle with controlled rollouts and rapid rollbacks. By the end of this course, you will be able to transform fragile prototypes into robust, reliable, and scalable production services.

This module establishes the foundational mindset that "performance lives in the pipeline." Learners will discover that a large language model (LLM) application is a multi-stage system where overall speed is dictated by the slowest component. They will learn to deconstruct a complex Retrieval-Augmented Generation (RAG) architecture, trace a user request through it, and use system diagrams to form an evidence-based hypothesis about the primary performance bottleneck.

涵盖的内容

2个视频1篇阅读材料2个作业

In this module, learners move from hypothesis to evidence. They will learn to use system logging and profiling data to quantify the precise latency contribution of each stage in an LLM pipeline. The focus is on designing small, reversible, and hypothesis-driven experiments to prove or disprove their initial findings and distinguish a performance bottleneck's root cause from its symptoms.

涵盖的内容

1个视频2篇阅读材料2个作业

This module bridges the gap between a working prototype and a resilient, production-ready service. Learners will design and manage declarative deployments using Helm and Kubernetes, package a multi-component RAG stack, and implement Horizontal Pod Autoscaling (HPA) for dynamic, cost-efficient scaling. They will also master the critical operational skills of performing controlled, zero-downtime rollouts and rapid rollbacks.