PHAi 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
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 discussionsRequirements
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 requiredDescription
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 decisionsCase 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 AgentWhat 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 projectsImportant Note
This course does NOT include practical coding or implementation.
There are
No hands-on coding projects No model training exercises No deployment labsInstead, the focus is entirely on
System design Architecture diagrams Engineering trade-offs Real world production thinkingPrerequisites
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 systemsHomepage