AWS Machine Learning Cost Optimization and Governance

Machine learning projects on AWS can quickly become expensive if resources are left unmanaged. Understanding how to track, govern, and optimize these costs is essential for running sustainable machine learning operations. This course equips you with the foundational knowledge to implement effective cloud financial management practices for your machine learning pipelines, helping you monitor expenses, enforce governance policies, and select the most cost-effective computing options. What you'll learn: - Understand core AWS cost management concepts and foundational cloud financial terminology - Track and allocate machine learning costs using AWS Cost Explorer and strategic resource tagging - Optimize compute resources for training and inference using AWS Compute Optimizer - Select cost-effective SageMaker purchasing options, including Spot Instances and Savings Plans - Implement governance guardrails and budget alerts to prevent unexpected cloud bills - Apply modern FinOps principles to manage the lifecycle of machine learning models efficiently You will start by exploring essential cost tracking tools and governance frameworks before diving into practical strategies for optimizing SageMaker training and deployment environments. Through written explanations and realistic scenarios, you will learn how to design and maintain budget-conscious machine learning workflows. This course is designed for beginners, cloud practitioners, and data scientists looking to manage AWS expenses, with no prior financial background required. Start reading today to build efficient, cost-effective machine learning workloads on AWS.