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.
Architect Resilient LLM Microservices for Scale
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Architect Resilient LLM Microservices for Scale
This course is part of Microservices Architecture for AI Systems Specialization
Instructor: LearningMate
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
Design and implement scalable, resilient microservice architectures for LLM apps using the 12-factor app methodology for fault tolerance in the cloud
Skills you'll gain
- Site Reliability Engineering
- Reliability
- Cloud Computing Architecture
- Software Architecture
- Service Recovery
- Service Management
- Dependency Analysis
- Microservices
- Data Storage Technologies
- Cloud-Native Computing
- LLM Application
- Maintainability
- Cloud Deployment
- Application Deployment
- Failure Analysis
- Configuration Management
- Solution Architecture
- Scalability
- Systems Architecture
- Software Development
Details to know
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Recently updated!
January 2026
Taught in English
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This course is part of the Microservices Architecture for AI Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in this course
This module provides a comprehensive guide to designing, evaluating, and documenting scalable and fault-tolerant microservices for LLM applications. You will be immediately immersed in a design review to understand the importance of resilience. You will then learn the core principles of the 12-Factor App methodology and multi-region deployment strategies, understand their application in practice, and use that knowledge to begin documenting a new inference service and assessing architectural risks.
What's included
1 video1 reading3 assignments
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