AWS Machine Learning Solutions: From Lifecycle to MLOps

Building successful machine learning systems requires more than just training a model; it demands a structured approach to the entire lifecycle, from data preparation to production monitoring. This course guides you through designing, deploying, and managing robust machine learning solutions on AWS. You will transition from understanding basic ML theory to structuring production-ready pipelines. You will learn how to select the right AWS services for each stage of the development lifecycle, evaluate model performance accurately, and implement foundational MLOps practices to keep your systems running smoothly. What you'll learn: - Understand the core phases of the machine learning lifecycle, from data ingestion to model deployment. - Configure AWS services to store data, train models, and host endpoints efficiently. - Evaluate model performance using key metrics and select the best algorithms for your business needs. - Apply modern MLOps principles to automate pipelines and monitor models in production. - Explore modern machine learning paradigms, including integration patterns for foundation models. The course begins with essential terminology and lifecycle concepts before walking you through structured written guides on model training, evaluation, and pipeline automation. You will practice these concepts through conceptual reviews and written exercises designed to build your engineering confidence. This course is designed for aspiring cloud engineers, developers, and data professionals who are new to machine learning on AWS. No prior machine learning or cloud architecture experience is required. Start building scalable, automated machine learning workflows today.