Architecting Scalable Cloud AI Infrastructure

Coursera via Coursera

Go to class Write review

Overview

Coursera Flash Sale

40% Off Coursera Plus for 3 Months!

Grab it

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.

Syllabus

Multi-Cloud Workload Analysis

You will learn the systematic analysis of workload characteristics to make data-driven decisions about optimal service selection across AWS, Azure, and GCP platforms.

System Architecture Evaluation

You will develop expertise in systematic frameworks for assessing existing system architectures to identify performance bottlenecks and resilience gaps before they impact production systems.

Enterprise Reference Architecture Design

You will learn to create professional reference architecture diagrams that integrate security controls, deployment automation, and operational monitoring into cohesive, enterprise-ready designs.

Service Dependency Risk Analysis

You will learn systematic dependency analysis techniques to identify and prevent cascade failures in AI system architectures. Through hands-on application of FMEA principles and dependency mapping tools, learners will develop the skills to evaluate service relationships, assess failure propagation risks, and implement targeted safeguards that maintain system reliability under stress.

Observability Metrics Optimization

You will develop expertise in RED metrics analysis (Rate, Errors, Duration) to systematically identify performance bottlenecks and prioritize optimization strategies in AI systems. By analyzing real performance data and applying strategic decision-making frameworks, learners will transform observability metrics into actionable improvements that enhance system performance and user experience.

Standardized Template Development

You will design and implement production-ready microservice templates that standardize logging, tracing, and security middleware across AI service ecosystems. Through practical template development exercises, learners will create reusable foundations that accelerate development velocity while ensuring operational consistency and enterprise-grade security standards.

Cloud Usage Analysis Foundation

You will learn systematic cloud cost analysis techniques by examining real AWS billing data to uncover hidden inefficiencies and develop data-driven optimization strategies.

Policy Effectiveness Evaluation

You will systematically assess governance frameworks by analyzing tagging compliance reports, measuring policy enforcement effectiveness, and identifying gaps that compromise cost control and security compliance.

Automation Script Creation

You will develop Infrastructure as Code solutions using Terraform and Sentinel to automate policy enforcement, transforming reactive governance into proactive prevention systems that maintain compliance without manual intervention.

Root Cause Analysis & Data Lineage

You will learn systematic data quality troubleshooting by understanding lineage tracking, analyzing metadata graphs, and applying root cause analysis methodologies to diagnose issues affecting GenAI model performance in enterprise environments.

Storage Optimization & Cost Analysis

You will develop expertise in cost-effective storage architecture design by analyzing workload access patterns, evaluating tiering strategies across different storage technologies, and creating quantified optimization recommendations that balance performance requirements with budget constraints for enterprise GenAI systems.

Platform Integration Blueprint

You will apply systematic approaches to unified data processing architecture design by analyzing platform integration patterns, creating technical blueprints that specify Kafka, Spark, and Flink interoperability, and developing Architecture Decision Records with deployment guidance for enterprise GenAI environments.

Project: Architecting Scalable Cloud AI Infrastructure

You will design a comprehensive cloud infrastructure platform for generative AI operations, learning how fundamental cloud architecture principles, microservices patterns, and cost management practices work together to create reliable AI systems. You'll understand how cloud service selection affects system performance, how microservices design impacts reliability, and how automated governance prevents cost overruns. Through hands-on infrastructure design, you'll see how these infrastructure decisions impact both performance and budget in real AI environments.