Architect Resilient Microservices for AI Success

This course is part of AI Systems Reliability & Security Specialization

Instructors: Harshita Gulati

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

3 modules

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

Tools you'll learn

Details to know

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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

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.

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

3 videosTotal 21 minutes

Data-Driven Decisions That Save Systems5 minutes
Performance Tuning Strategies for AI System Bottlenecks6 minutes
Building Performance Analysis Dashboards for RED Metrics10 minutes

2 readingsTotal 20 minutes

RED Metrics Framework for AI System Performance Analysis10 minutes
System Monitoring Strategies for Proactive Performance Management10 minutes

2 assignmentsTotal 15 minutes

RED Metrics Analysis for System Optimization10 minutes
Observability Metrics Evaluation5 minutes

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

3 videosTotal 18 minutes

Template-Driven Development at Scale4 minutes
Implementing Middleware Integration in Microservice Templates9 minutes
Building Production-Ready Microservice Templates with Integrated Middleware5 minutes

1 readingTotal 10 minutes

Microservice Template Architecture for Operational Consistency10 minutes

3 assignmentsTotal 27 minutes

Design a Comprehensive Microservice Template for AI Workloads12 minutes
Template Development - Knowledge Check5 minutes
Comprehensive Microservice Resilience Architecture Assessment10 minutes

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Instructors

Harshita Gulati

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3 Courses862 learners

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