Microservices Architecture for AI and LLM Applications

Deploying artificial intelligence and Large Language Models (LLMs) in production requires more than just writing a prompt; it demands a scalable, resilient architecture. This text-based course guides you through transitioning from monolithic scripts to modular, production-ready microservices tailored for AI. By reading the conceptual guides and studying the practical code examples, you will understand how to structure, containerize, and connect AI services. You will gain the confidence to design systems that handle heavy AI workloads, manage API rate limits, and scale seamlessly under load. What you'll learn: Understand the core principles of microservices architecture and how they apply to AI workloads; Design robust API endpoints using modern Python frameworks to serve machine learning models and LLMs; Implement basic containerization to package your AI services for consistent deployment; Configure asynchronous communication patterns to handle long-running AI inference tasks; Apply rate limiting, caching, and error-handling strategies to manage external AI API costs and downtime; Integrate vector database lookups and retrieval-augmented generation patterns into a microservices workflow. The course begins with foundational terminology, comparing monolithic design with microservices, before moving into step-by-step written walkthroughs of containerization, API design, and asynchronous message queues. You will progress from basic concepts to reading and analyzing production-grade architecture patterns. This course is designed for software developers, data scientists, and technical beginners who want to transition their AI prototypes into scalable systems. No prior experience with microservices or containerization is required, though a basic understanding of Python is helpful. Start reading today to build resilient, production-ready architectures for your AI applications.