Microservices Architecture for AI Systems
About this course
This Specialization equips software developers, ML engineers, and system architects with the skills to design, build, and deploy production-grade AI systems using microservices architecture. Beginning with LLM fundamentals and Retrieval-Augmented Generation (RAG) techniques, learners progress through architecture design and trade-off analysis, resilient microservice patterns using the 12-factor app methodology, and test-driven development practices. The program culminates with hands-on experience deploying scalable LLM applications using Kubernetes and Helm, integrating services via gRPC and Protobuf, and implementing production monitoring with Prometheus. By completion, learners will be able to transform AI prototypes into robust, enterprise-ready systems that scale on demand and withstand real-world failures.
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What you'll learn
- designing AI systems using microservices architecture
- implementing resilient microservice patterns
- deploying applications with Kubernetes and Helm
- integrating services using gRPC and Protobuf
- applying production monitoring techniques
Course objectives
- equip learners to transform AI prototypes into enterprise-ready systems
- develop test-driven development practices
- analyze architecture design trade-offs
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