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CS 203: Enterprise Cloud Architecture & DevOps
Course Code CS 203
Course Name Enterprise Cloud Architecture & DevOps
Department Computer Science
Semester Offered Odd (Term 3 - Shanghai)
Tuition Hours 30 hours
Course Level Intermediate to Advanced
Pre-requisite CS 104: Data Engineering for Emerging Markets
Co-requisite -
Course Objective Building a product is only half the job. The real challenge is making sure it runs reliably, scales under load, and does not break when real users depend on it.

This course teaches students how to design and operate systems on cloud infrastructure that can support real businesses. The focus is on practical architecture decisions, not theoretical diagrams.

Students will learn how to deploy, manage, and scale applications using modern cloud platforms, containerization, and DevOps practices. They will also understand security, compliance, and reliability requirements that come with enterprise systems.

By the end of the course, students will be able to take any working product and turn it into a production-grade system, capable of serving users across regions with minimal downtime.
Course Philosophy This course emphasizes
  • Reliability over novelty
  • Systems thinking over tool-specific knowledge
  • Automation over manual processes
Students will learn that infrastructure is not an afterthought. It is part of the product. The goal is to build systems that stay alive, recover from failure, and scale without constant intervention.
Course Learning Outcomes Upon successful completion of this course, students will be able to:
  • Understand cloud computing fundamentals across major providers like AWS, GCP, and Azure.
  • Design scalable system architectures for real-world applications.
  • Use containerization tools such as Docker to package applications.
  • Orchestrate services using Kubernetes for scalable deployments.
  • Implement CI/CD pipelines for automated testing and deployment.
  • Apply infrastructure as code principles to manage cloud resources.
  • Ensure system reliability and fault tolerance across deployments.
  • Understand basic security and compliance requirements such as SOC2 and ISO27001.
  • Deploy multi-region systems to serve global users efficiently.
Course Author Sagar Udasi
MSc Statistics and Data Science with Computational Finance from The University of Edinburgh.
Contact: sagar.l.udasi@gmail.com
Course Organiser TBD
Details will be updated before course commencement.
No. Lecture Title Concepts Covered Lecture Objective
01 Your App Works… Until Users Show Up Scaling problems, system failures Show why infrastructure matters when usage increases.
02 What Is The Cloud Actually? Cloud fundamentals, compute, storage Build foundational understanding of cloud systems.
03 Designing Systems That Don’t Break System architecture basics Teach how to think in terms of systems, not just code.
04 One Server Is Never Enough Load balancing, horizontal scaling Prepare for handling increasing user load.
05 Containers: Packaging Your World Docker, containerization Standardize environments for reliable deployment.
06 Running Many Things At Once Kubernetes basics, orchestration Manage distributed systems efficiently.
07 Deploying Without Fear CI/CD pipelines, automation Enable rapid and safe deployments.
08 Infrastructure As Code IaC tools, reproducibility Automate infrastructure setup and management.
09 Databases At Scale Managed databases, replication Handle large-scale data reliably.
10 Handling Failures Gracefully Fault tolerance, retries Build systems that recover instead of crashing.
11 Observability: Seeing What’s Broken Logging, monitoring, metrics Detect and diagnose issues in production systems.
12 Security Is Not Optional Authentication, encryption, best practices Protect systems and user data.
13 What Enterprises Actually Care About Compliance, SOC2, ISO27001 basics Introduce real-world requirements for enterprise systems.
14 Serving Users Across The World Multi-region deployment, latency Optimize systems for global access.
15 Cost Matters More Than You Think Cloud cost optimization Design efficient systems that are financially viable.
16 From Prototype To Production Migration strategies Transition from small apps to production systems.
17 Scaling AI Systems Serving ML models at scale Connect infrastructure to AI workloads.
18 Case Study: Global AI Product Infrastructure Real-world architecture breakdown Apply concepts to realistic product scenarios.
19 Building Your Own Cloud Architecture End-to-end system design Students design infrastructure for their projects.
20 Demo Day: Does It Stay Alive Under Load? Presentations, stress testing Evaluate systems based on reliability and scalability.
Component Weightage
Infrastructure Assignments (4 total) 30%
CI/CD Pipeline Project 20%
Final Project: Scalable Cloud Deployment 30%
Viva + Architecture Review 20%
Type Resource Provider
Lecture AWS Cloud Practitioner Essentials AWS
Lecture Kubernetes for Developers Google Cloud
Reading Designing Data-Intensive Applications Martin Kleppmann
Reading The Phoenix Project Gene Kim
Documentation Docker Documentation docker.com
Documentation Kubernetes Documentation kubernetes.io