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.
Analyze & Deploy Scalable LLM Architectures
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您将获得的技能
- Kubernetes
- Containerization
- Model Deployment
- Configuration Management
- MLOps (Machine Learning Operations)
- Scalability
- Release Management
- Performance Testing
- Retrieval-Augmented Generation
- Infrastructure as Code (IaC)
- Application Deployment
- Continuous Delivery
- Performance Analysis
- Systems Analysis
- Cloud Deployment
- Analysis
- LLM Application
- Application Performance Management
- Large Language Modeling
- Performance Tuning
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Microservices Architecture for AI Systems 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有3个模块
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.
涵盖的内容
2个视频2篇阅读材料2个作业
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