Designing Production LLM Architectures

This course is for ML engineers, solutions architects, and senior developers who build robust infrastructure powering large language models. This course teaches you how to design, deploy, and maintain the complex, interconnected systems required for scalable, resilient, and cost-effective LLM applications in the real world. You will learn to think like an architect, starting with foundational design choices. Using sequence diagrams and structured analysis, you will compare synchronous and asynchronous architectures and evaluate the critical trade-offs between self-hosting open-source models and using managed APIs, considering total cost of ownership, latency, and data privacy. The course then dives deep into building for resilience and scale, applying the 12-factor app methodology to design stateless, configurable microservices. You’ll learn to analyze multi-region deployment strategies for fault tolerance and to use container orchestration manifests like Helm to deploy scalable applications capable of handling production workloads. Finally, you’ll master the data backbone of your system by designing automated data pipelines with tools like Airflow and learning to manage the complexities of schema evolution

• Compare synchronous and asynchronous architectures and apply twelve-factor app principles
• Analyze multi-region deployments and improve resilience through fault analysis
• Build automated data workflows and assess schema evolution impacts
• Evaluate trade-offs between self-hosted models and managed APIs

• Basic understanding of AI and machine learning concepts
• Willingness to engage in applied exercises or case studies