Analyze & Deploy Scalable LLM Architectures

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

• Analyze multi-stage architectures such as RAG to diagnose and quantify performance bottlenecks
• Design and deploy containerized LLM applications using declarative Helm charts on Kubernetes
• Implement Horizontal Pod Autoscaling and manage controlled rollouts and rollbacks

• Basic familiarity with topic and terminology
• Willingness to practice via applied exercises