Subject Overview

An advanced, provider-neutral and comparative study of cloud platforms and DevOps using AWS, Google Cloud and Microsoft Azure. The subject integrates cloud architecture, automation, delivery pipelines, cloud-native systems, reliability, security, governance, economics, AI-enabled operations and organizational transformation.

Learning Goal

Enable students to design, automate, secure, operate and govern resilient multi-cloud solutions; evaluate AWS, GCP and Azure services; apply DevOps and SRE practices; and make evidence-based technical, financial and strategic decisions.

Syllabus

1. Cloud Computing and DevOps Foundations

Learning Objective: Explain cloud characteristics, service and deployment models, virtualization, distributed systems, DevOps principles, CALMS, value-stream thinking, Agile relationships and the shared-responsibility model.

Category: foundation

2. AWS, GCP and Azure Platform Models

Learning Objective: Compare global infrastructure, regions, availability zones, account and resource hierarchies, service taxonomies, management consoles, CLIs, SDKs, quotas, SLAs and provider-selection criteria.

Category: foundation

3. Cloud Networking and Hybrid Connectivity

Learning Objective: Design secure virtual networks using VPCs/VNets, subnets, routing, DNS, load balancing, firewalls, private endpoints, VPNs, peering, transit architectures, CDNs and hybrid connectivity.

Category: core

4. Identity, Access and Secrets Management

Learning Objective: Apply IAM principles through users, roles, service identities, policies, federation, SSO, RBAC/ABAC, least privilege, privileged access, key management, certificates and secrets rotation across providers.

Category: core

5. Compute, Storage, Databases and Managed Services

Learning Objective: Evaluate virtual machines, autoscaling, object/block/file storage, relational and NoSQL databases, caching, messaging and managed services based on performance, availability, consistency, cost and operational burden.

Category: core

6. Infrastructure as Code and Configuration Automation

Learning Objective: Build reproducible environments using Terraform, CloudFormation, ARM/Bicep, Google Cloud tools, Ansible and policy as code; manage state, modules, testing, drift, secrets and environment promotion.

Category: core

7. Continuous Integration, Delivery and Deployment

Learning Objective: Design secure CI/CD pipelines using source control, branching strategies, automated testing, artifact repositories, environment controls and rolling, blue-green, canary and feature-flag deployments.

Category: core

8. Containers, Kubernetes and Serverless Platforms

Learning Objective: Build and operate containerized, Kubernetes and serverless workloads using EKS, GKE, AKS and provider functions; address orchestration, scaling, service discovery, ingress, persistence and workload portability.

Category: application

9. Observability, SRE and Incident Management

Learning Objective: Implement logs, metrics, traces, dashboards and alerting; apply SLIs, SLOs, error budgets, toil reduction, capacity analysis, incident command, postmortems and continuous reliability improvement.

Category: application

10. Resilient Cloud-Native Architecture

Learning Objective: Design scalable, loosely coupled systems using microservices, APIs, event-driven patterns, queues and managed services; evaluate fault tolerance, backup, disaster recovery, RTO/RPO and chaos engineering.

Category: advanced

11. DevSecOps, Compliance and Cloud Governance

Learning Objective: Integrate threat modeling, vulnerability management, supply-chain security, zero trust, encryption, security testing and runtime protection with governance frameworks, auditability, privacy, data sovereignty and responsible technology ethics.

Category: strategic

12. FinOps, Sustainability and Operational Analytics

Learning Objective: Analyze billing and telemetry to forecast demand, allocate costs, manage budgets, optimize utilization and unit economics, assess carbon impact and balance cost, performance, reliability and business value.

Category: strategic

13. Data, AI, MLOps and Intelligent Operations

Learning Objective: Explain cloud data and analytics services, data pipelines, lakehouse patterns and MLOps lifecycles; apply AIOps and generative AI to anomaly detection, incident analysis and automation while managing bias, privacy and model risk.

Category: contemporary

14. Platform Engineering, GitOps and Multi-Cloud Operations

Learning Objective: Design internal developer platforms, golden paths, self-service environments and GitOps workflows; evaluate service meshes, policy automation, developer experience, portability, lock-in and multi-cloud operating trade-offs.

Category: contemporary

15. Cloud Strategy, Migration and Future Directions

Learning Objective: Develop business cases, portfolio assessments and migration roadmaps using the 6Rs/7Rs and cloud adoption frameworks; lead operating-model change, skills development and vendor management while evaluating edge, confidential, sovereign and autonomous cloud trends.

Category: strategic