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.
Architect Resilient Microservices for AI Success
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Architect Resilient Microservices for AI Success
This course is part of AI Systems Reliability & Security Specialization
Instructors: Harshita Gulati
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Proactive failure analysis builds anti-fragile systems that improve under stress instead of collapsing.
Data-driven optimization using RED metrics (Rate, Errors, Duration) drives performance gains and prevents outages.
Standardized microservice templates speed development while ensuring operational consistency and security compliance.
Resilient architecture comes from defining system boundaries, planning for failures, and implementing full observability.
Skills you'll gain
- Performance Tuning
- Service Level
- Application Performance Management
- Systems Development
- Performance Analysis
- Dependency Analysis
- Distributed Computing
- AI Security
- Continuous Monitoring
- Failure Mode And Effects Analysis
- System Monitoring
- Failure Analysis
- Performance Metric
- Data-Driven Decision-Making
- Site Reliability Engineering
- Microservices
Details to know
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Recently updated!
January 2026
Taught in English
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This course is part of the AI Systems Reliability & Security Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
Learners will master 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.
What's included
2 videos1 reading1 assignment
Learners 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.
What's included
3 videos2 readings2 assignments
Learners 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.
What's included
3 videos1 reading3 assignments
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