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.

As AI applications are built at record speed, many teams are accumulating significant "technical debt," leading to brittle, unpredictable, and expensive systems. "Refactor and Test LLM Microservices" is an intermediate course designed for software developers and ML engineers who want to build production-grade AI applications that last. This course moves beyond notebooks and scripts to instill the software engineering discipline required for robust microservices. You will master Test-Driven Development (TDD), learning to write failing unit tests before implementing new API endpoints to ensure correctness from the start. You will also learn to act on code-review feedback by systematically refactoring complex code, breaking down monolithic functions into clean, readable, and maintainable modules. Through hands-on labs in a VS Code environment, you will refactor a legacy service and build a new, fully tested API endpoint, ensuring your work is not just functional, but also scalable and reliable.

Selecting the appropriate architecture for a large language model (LLM) application is a critical decision for any technical team, influencing costs, performance, and security. The course "Design, Compare and Analyze LLM Architectures" is tailored for engineers, architects, and technical leads involved in these pivotal "build vs. buy" assessments. It offers a structured approach to designing and justifying system architectures. Learners will learn to enhance their visual communication skills by creating sequence diagrams that illustrate the trade-offs between synchronous and asynchronous processing flows. The course also emphasizes strategic analysis of deployment options, comparing self-hosting an open-source model with utilizing a managed API. Key skills developed include calculating Total Cost of Ownership (TCO), evaluating latency, and understanding data privacy implications, enabling participants to make informed, business-focused recommendations. By the end of this course, you will be able to confidently design, defend, and document your architectural choices to any stakeholder.

This intermediate course teaches you how to design scalable, reliable AI systems that work in real-world production environments. You’ll learn how to build end-to-end architectures that meet throughput, latency, and fault-tolerance goals, and you’ll move from conceptual design to detailed component diagrams and interface specifications. Using industry patterns adopted by modern ML teams, you’ll practice estimating QPS, defining autoscaling rules for the inference layer, structuring data flow between the feature store and model API, and instrumenting your system with a monitoring stack. By the end of the course, you will have created a complete architecture document—including diagrams and interface definitions—that engineering teams can use to implement a scalable AI product.