AWS Serverless and Event-Driven Pipelines for Machine Learning

Modern machine learning models require scalable, reliable, and cost-effective infrastructure to run inference and orchestrate workflows. Transitioning from local scripts to production-ready, event-driven pipelines on AWS allows you to automate execution without managing servers. Through this written course, you will learn how to design, configure, and connect serverless AWS components to orchestrate machine learning tasks. You will start with the fundamental concepts of event-driven architecture and serverless computing before moving on to practical configurations for data processing and model orchestration. What you'll learn: - Understand the core principles of serverless computing and event-driven architecture. - Configure AWS Lambda functions to trigger lightweight data preprocessing and model inference tasks. - Manage asynchronous message queues and decoupling with SQS to ensure pipeline reliability. - Coordinate complex, multi-step ML workflows using Step Functions state machines. - Route real-time events across your pipeline using EventBridge rules. - Apply modern infrastructure-as-code concepts and observability practices to monitor serverless workloads. The course begins with foundational definitions of serverless services and event-driven patterns. You will then progress through step-by-step written guides and architectural scenarios, learning how to connect each component into a cohesive, automated machine learning pipeline. This text-based course is designed for software developers, data scientists, and cloud beginners who want to build automated workflows on AWS. No prior cloud architecture experience is required. Start reading today to master the building blocks of serverless machine learning orchestration.