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 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.

"Integrate and Optimize AI Services Seamlessly" is an applied, intermediate-level course designed for engineers and ML practitioners who want to build reliable, production-ready AI systems. Across focused, hands-on lessons, the course explores how real-world services communicate using APIs, message queues, and structured serialization formats. Learners gain practical experience integrating prediction services with gRPC and protobuf, improving consistency, performance, and cross-language compatibility. The course also guides participants through deployment health essentials, including interpreting Prometheus metrics, spotting early warning signs during canary releases, and making safe decisions to stabilize or roll back new versions. Through real scenarios, interactive activities, and expert-led demos, students develop the confidence to ship AI services that are fast, resilient, and operationally sound in modern distributed environments.