Architecting Scalable Cloud AI Infrastructure

Enterprise AI systems require cloud infrastructure that scales globally while controlling cost and reliability. This course equips you with architecture skills to design multi-cloud AI platforms, build resilient microservices, automate governance, and optimize data systems for generative AI workloads. You will learn to make infrastructure decisions across AWS, Azure, and GCP, identify failure risks in distributed systems, implement automated cost controls, and architect data pipelines that balance performance with budget constraints. Through hands-on enterprise projects, you will create production-ready blueprints with security zones, CI/CD pipelines, and observability stacks. You will also build microservice templates with standardized logging and tracing, develop compliance automation scripts, and design unified data architectures integrating Kafka and Spark. These skills prepare you for roles as cloud architects, site reliability engineers, and infrastructure leaders deploying AI systems at scale. By the end of the course, you will be able to prevent failures through proactive design, reduce cloud expenses through automation, and build systems that remain resilient under stress

• Design multi-cloud AI architectures with scalability and security
• Build resilient microservices using dependency analysis and RED metrics
• Automate cloud cost optimization and governance
• Create operational excellence frameworks with monitoring and incident response

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work