AI System Design for Engineers

高并发AI系统架构设计实战

专为工程师打造的生产级AI系统设计课程，涵盖Kafka、Kubernetes、LLM与多智能体架构，助您搭建高并发、低成本、低延迟的端到端AI系统。

《高并发生产级 AI 系统设计与架构》 课程简介，专为资深软件工程师、架构师及 AI 工程师量身打造。课程完全摒弃了基础的代码编写与模型训练，核心专注于架构设计（System Design）与工程权衡（Trade-offs），教您如何利用 Kafka、Kubernetes 和大规模机器学习技术，搭建能承受数百万级流量、兼顾成本与低延迟的生产级 AI 系统。

核心内容

全栈 AI 架构设计： 掌握从数据管道、分布式训练、到高并发推理（Inference）的端到端架构设计，涵盖实时（Real-time）与批处理（Batch）推理系统。

前沿 LLM 与智能体架构： 深入设计现代大语言模型应用，包括检索增强生成（RAG）系统、单智能体以及多智能体协同（Multi-agent）工作流。

云原生与事件驱动风控： 应用 Kafka 搭建事件驱动型 AI 管道，利用 Kubernetes (K8s)、微服务及向量数据库实现高可用、可扩展的云原生 AI 架构。

Published 6/2026
Created by Aritra Basak
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 36 Lectures ( 7h 5m ) | Size: 6.8 GB

Design production ready AI systems through real world case studies on LLMs, AI Agents, Kafka, Kubernetes and ML at scale

What you’ll learn
⚡ Design production grade end to end AI systems including data pipelines, training, inference and deployment
⚡ Build scalable machine learning, LLM and agent based architectures for real world applications
⚡ Design real time and batch inference systems that handle large scale traffic and millions of requests
⚡ Apply Kafka, Kubernetes and cloud native patterns to build event driven AI systems
⚡ Optimize AI systems for performance, cost efficiency, scalability and reliability in production
⚡ Design modern LLM applications including RAG systems, AI agents and multi agent workflows
⚡ Gain confidence in AI system design interviews and real-world architecture discussions

Requirements
❗ Good understanding of software engineering fundamentals such as APIs, databases, and basic system design concepts
❗ Basic knowledge of machine learning, deep learning, and AI engineering workflows including model training and inference
❗ This is not a beginner friendly course, so a solid foundation in both software engineering and AI engineering is required

Description

AI System Design for Engineers
Learn how to design production ready, scalable AI systems through real world architecture case studies used in modern AI companies. This course focuses on system design thinking for AI workloads, not coding or implementation.

You will learn how real AI systems are designed, scaled, and optimized for millions of users using technologies like LLMs, RAG, Kafka, Kubernetes, and ML pipelines.

What You Will Learn

✨ How to design end to end AI systems from requirements to architecture

✨ Real world AI system design case studies used in production companies

✨ Scalable ML and LLM inference system design

✨ Event driven architectures using Kafka for AI pipelines

✨ How vector databases and caching systems are used in AI applications

✨ Microservices architecture for AI powered products

✨ Handling large scale traffic, latency, and cost optimization

✨ Designing multi agent and LLM based systems

✨ Trade-offs in real production AI system design decisions

Case Studies Covered

✨ Google CTR Prediction System

✨ HubSpot User Clustering System

✨ Facebook Content Moderation System

✨ AI Grammar Checker SaaS Application

✨ AI Interview Chatbot System

✨ Smart Car Parking System (Computer Vision SaaS)

✨ Deep Research AI Agent (Multi-Agent System)

✨ Autonomous Travel Booking Agent

What to Expect from This Course

✨ Focus on architecture and system design only

✨ Deep dive into real world production AI systems

✨ Learn how senior engineers think about scalability and reliability

✨ Understand how AI systems are designed at companies like Google, Meta, and SaaS startups

✨ Gain confidence in designing complex AI architectures in interviews and real projects

Important Note

This course does NOT include practical coding or implementation.

There are

✨ No hands-on coding projects

✨ No model training exercises

✨ No deployment labs

Instead, the focus is entirely on

✨ System design

✨ Architecture diagrams

✨ Engineering trade-offs

✨ Real world production thinking

Prerequisites

This is an intermediate level course.

You should already be familiar with

✨ Basic software engineering concepts (APIs, databases, caching)

✨ Fundamentals of Machine Learning and Deep Learning

✨ Basic understanding of cloud and deployment concepts

✨ Familiarity with containers (Docker, Kubernetes is a plus)