Building Serverless Machine Learning Pipelines on AWS

Deploying machine learning models into production often requires complex infrastructure management. By shifting to a serverless architecture, you can automate your ML workflows efficiently without provisioning or managing servers. This course guides you through the foundational concepts of serverless MLOps on AWS. You will learn how to connect data processing, model inference, and monitoring into a cohesive, automated pipeline that scales on demand. What you'll learn: - Understand the core terminology of serverless architectures and MLOps orchestration. - Configure AWS Step Functions to coordinate multi-step machine learning workflows. - Deploy containerized ML code using ECR repositories and AWS Lambda. - Automate pipeline triggers using EventBridge rules and schedules. - Monitor pipeline health and log execution metrics using CloudWatch. - Apply modern infrastructure-as-code concepts to deploy your pipeline repeatably. You will start by exploring essential definitions and serverless design patterns before diving into step-by-step configuration guides. Through written explanations and practical code examples, you will build a complete workflow from data ingestion to model prediction. This course is designed for aspiring data scientists, developers, and cloud beginners who want to learn automated machine learning deployment. No prior AWS or MLOps experience is required. Start reading today to master the fundamentals of serverless ML orchestration.