AWS Compute Foundations for Machine Learning Workloads

Running machine learning models requires significant computational power, and choosing the wrong infrastructure can quickly lead to slow performance and runaway cloud bills. This text-based course guides you through selecting, configuring, and optimizing AWS compute resources specifically tailored for machine learning tasks. Through clear written explanations and practical configurations, you will transition from cloud basics to confidently managing compute environments. You will learn how to align hardware specifications with model needs, utilize modern container workflows, and implement smart cost-saving strategies. What you'll learn: - Understand the core hardware requirements of machine learning workloads, including CPU, GPU, and specialized accelerators like Trainium and Inferentia. - Select the optimal EC2 instance families for training and deploying diverse machine learning models. - Optimize infrastructure costs using AWS pricing models, including Spot Instances and Savings Plans. - Configure containerized environments using ECS and EKS to scale machine learning microservices. - Apply modern infrastructure-as-code concepts to provision reproducible compute environments. - Implement basic monitoring and observability to track compute performance and resource utilization. The course begins with foundational definitions of cloud compute and machine learning hardware requirements, ensuring you understand the reasoning behind infrastructure choices. You will then progress through detailed text guides on instance selection, pricing optimization, container deployment, and modern scaling strategies. This course is designed for beginner cloud engineers, aspiring MLOps practitioners, and developers looking to deploy machine learning models on AWS. No prior cloud infrastructure experience is required, though a basic familiarity with machine learning concepts is helpful. Start building cost-effective and scalable compute environments for your machine learning workloads today.