This module explores building resilient, scalable architectures for LLM applications. You will apply 12-factor app methodology to design portable, cloud-native microservices, mastering stateless design and dependency management. The curriculum bridges theory and practice by evaluating multi-region deployment strategies for fault tolerance and high availability. You'll learn to analyze failover mechanisms and mitigate architectural risks before production. By the end, you’ll be equipped to document reliable, future-proof AI systems. Prerequisites include a foundational understanding of cloud concepts (regions/zones) and microservice basics (containers/APIs).

This module teaches how to transition LLM prototypes into production-grade services. You will learn to analyze multi-stage architectures like RAG to identify and quantify performance bottlenecks using evidence-based metrics. The curriculum focuses on mastering Kubernetes deployment through declarative Helm charts and implementing Horizontal Pod Autoscaling (HPA) to manage unpredictable traffic. By studying deployment lifecycles, including controlled rollouts and rapid rollbacks, you will gain the skills to transform fragile prototypes into resilient, scalable, and reliable production systems capable of handling real-world loads.

In today's dynamic data landscape, pipelines often break when source data structures change unexpectedly—a problem known as schema drift. This module tackles that challenge head-on, teaching you how to design and automate data pipelines that can gracefully handle schema evolution using Apache Airflow. By the end, you will be equipped to create resilient, scalable, and fully automated data pipelines that are built to withstand the complexities of real-world data environments.

In the module, you will step into the high-stakes role of a senior systems engineer tasked with diagnosing a failing AI service. A critical Retrieval-Augmented Generation (RAG) system is plagued by high latency and intermittent outages, and you must get to the root of the problem. Using architectural diagrams, system logs, and performance metrics, you will analyze the system’s design to identify the primary performance bottleneck and the most significant single point of failure. Your analysis will culminate in a concise, two-paragraph report for stakeholders, pinpointing the critical issues and recommending targeted fixes to restore stability and performance.