Architect Resilient LLM Microservices for Scale

Coursera MOOC / Non-credit USD 49
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Architect Resilient LLM Microservices for Scale

About this course

This course is designed for intermediate-level software developers, cloud engineers, and system architects responsible for building and scaling LLM applications. As AI systems become more complex, a resilient and scalable architecture is no longer a luxury—it's a necessity. This course provides a focused, practical guide to designing robust, cloud-native microservices that can withstand failure and scale on demand. You will learn to apply the proven 12-factor app methodology to create services that are portable, maintainable, and ready for continuous deployment. Through expert instruction and real-world case studies, you will master the principles of stateless design, externalized configuration, and dependency management. The course then moves from theory to practice, challenging you to evaluate multi-region deployment strategies for fault tolerance and high availability. You will learn to analyze failover mechanisms, assess data replication strategies, and identify architectural risks before they impact production. By the end of this course, you will be equipped to design and document resilient microservice architectures that ensure your LLM applications are not just powerful, but also reliable and built for the future. To successfully complete this course, a working knowledge of core cloud concepts (regions, zones, and elasticity) and microservice basics (services, APIs, and containers) is recommended.

What you'll learn

  • design robust cloud-native microservices
  • apply the 12-factor app methodology
  • evaluate multi-region deployment strategies
  • analyze failover mechanisms
  • assess data replication strategies

Course objectives

  • equip students to design resilient microservice architectures
  • prepare students for continuous deployment of applications
  • help students identify architectural risks before they impact production

Skills you'll gain

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