Learning AI System Design for Engineers

[voska89]

AI System Design for Engineers
Published 6/2026
Created by Aritra Basak
MP4 | Video: h264, 1920x1080 | 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)
Who this course is for
Software engineers who want to transition into AI engineering and learn how to build production grade AI systems
Machine learning engineers and AI engineers who want to strengthen their system design and large scale AI architecture skills
Experienced developers preparing for AI system design interviews or roles in AI infrastructure, LLM applications and scalable ML systems
Homepage

https://www.udemy.com/course/ai-system-design-for-engineers

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