Microservices Architecture for AI Systems

In this course, you’ll learn how to integrate enterprise data with advanced large language models (LLMs) using Retrieval-Augmented Generation (RAG) techniques. Through hands-on practice, you’ll build AI-powered applications with tools like LangChain, FAISS, and OpenAI APIs. You’ll explore LLM fundamentals, RAG architecture, vector search optimization, prompt engineering, and scalable AI deployment to unlock actionable insights and drive intelligent solutions. This course is ideal for data scientists, machine learning engineers, software developers, and AI enthusiasts who are eager to harness the power of large language models (LLMs) in enterprise applications. Whether you’re building AI solutions for customer service, content generation, knowledge management, or data retrieval, this course will equip you with practical skills to bridge the gap between enterprise data and cutting-edge AI capabilities. To succeed in this course, learners should have a basic understanding of machine learning principles and some hands-on experience working with large language models (such as using OpenAI APIs or Hugging Face models). Proficiency in Python programming is essential, along with a basic understanding of how APIs work. These foundational skills will ensure you can comfortably follow along with the hands-on projects and technical demonstrations throughout the course. By the end of this course, learners will be able to seamlessly integrate large language models (LLMs) with enterprise data applications, enabling smarter and more context-aware AI systems. They will gain the skills to evaluate and apply retrieval-augmented generation (RAG) techniques to enhance both the accuracy and efficiency of information retrieval and content generation processes. Additionally, learners will master the art of prompt refinement to optimize the quality and relevance of AI-generated responses, and they will be equipped to design and deploy scalable, LLM-powered solutions that address complex real-world challenges faced by modern enterprises.

Analyze & Deploy Scalable LLM Architectures is an intermediate course for ML engineers and AI practitioners tasked with moving large language model (LLM) prototypes into production. Many powerful models fail under real-world load due to architectural flaws. This course teaches you to prevent that. You will learn to analyze multi-stage architectures such as RAG to diagnose and quantify performance bottlenecks with evidence, not assumptions. You will then master the tools of production-grade operations, designing and writing declarative Helm charts to deploy containerized LLM applications on Kubernetes. The curriculum focuses on building resilient, scalable systems by implementing Horizontal Pod Autoscaling (HPA) to handle unpredictable traffic and managing the full deployment lifecycle with controlled rollouts and rapid rollbacks. By the end of this course, you will be able to transform fragile prototypes into robust, reliable, and scalable production services.

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.