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.
Applied Learning Project
Throughout this Specialization, learners complete hands-on projects that mirror real-world enterprise challenges. Projects include building RAG-powered applications with LangChain and vector databases, creating sequence diagrams to analyze synchronous vs. asynchronous processing trade-offs, designing multi-region deployment strategies for fault tolerance, refactoring legacy code using TDD principles, writing Helm charts for Kubernetes deployments, and implementing canary release strategies with Prometheus monitoring. These practical exercises ensure learners can architect, test, deploy, and operate scalable AI systems in production environments.
