Architect Resilient Microservices for AI Success

A single authentication service hiccup lasting 30 seconds cascaded through an entire AI platform for three hours, costing millions in revenue—all because engineering teams hadn't mapped their service dependencies or implemented systematic resilience practices. This Short Course was created to help ML and AI professionals architect resilient distributed systems that power AI systems at scale. By completing this course you'll be able to proactively identify cascading failure risks, leverage RED metrics to prioritize system optimizations, and create standardized templates that accelerate development while ensuring operational consistency. By the end of this course, you will be able to: • Analyze service dependencies to identify potential cascading failure risks • Evaluate observability metrics to prioritize system optimizations • Create a microservice template with standardized logging, tracing, and security middleware This course is unique because it transforms reactive engineering teams into proactive ones by combining systematic dependency analysis, data-driven optimization, and standardized development frameworks into anti-fragile systems that improve under stress. To be successful, you should have basic understanding of distributed systems, microservices concepts, system monitoring tools, and software engineering principles

• Understand proactive failure analysis to build anti-fragile systems
• Apply RED metrics for data-driven performance optimization
• Create standardized microservice templates for development and security
• Design resilient architectures by defining system boundaries and observability

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