New digital courses for how to optimize AI compute and containers on AWS

The compute you choose determines how fast you can complete and ship AI applications or run containers at scale. It also determines how much you pay to complete that task. AWS offers a broad portfolio of purpose-built chips and instances. Understanding which one to use and when to use it is one of the first decisions you’ll make. In these two courses, you’ll learn how to match the right AWS compute to your AI workloads. You’ll also learn how to boost your Amazon Elastic Kubernetes Service (Amazon EKS) efficiency to improve both performance and cost.

Designed for decision-makers, developers, cloud architects, cloud operators, DevOps engineers, machine learning (ML) engineers, and platform engineers, these courses help you understand and use AWS AI compute and container technologies to meet your business needs.

Assembling the Complete AI Stack Optimizing Your AI Hardware on AWS

Learn to select the right AWS hardware for each stage of the AI pipeline, from data processing through training, inference, and application deployment. You’ll survey the full range of AWS compute options for AI workloads, including AWS Graviton, AWS Trainium and AWS Inferentia, GPU-based instances, and Intel- and AMD-based instances. You’ll also explore how to match specific pipeline stages to the most suitable compute option and apply those decisions effectively. You’ll understand when to use each instance type to balance cost and performance across your AI workloads.

Course level: Advanced

Duration: 2 hours

Course outline:

• The AWS AI stack
• Decision framework — how to select instances by model size
• Cost and capacity planning for Amazon Elastic Compute Cloud (Amazon EC2) instances
• Putting it together — the AI pipeline
• Practice — building an internal AI assistant
• Practice — startup inference deployment
• Practice — model customization

Boost Amazon EKS Efficiency — EKS Auto Mode, AWS Graviton, and Amazon EC2 Spot

Learn to maximize Amazon EKS cost efficiency and operational simplicity through three complementary technologies: EKS Auto Mode, AWS Graviton processors, and Amazon EC2 Spot Instances. You’ll gain insights into how to automate cluster infrastructure with EKS Auto Mode, along with the distinct strengths each technology brings to a containerized workload. You’ll also explore how to migrate workloads to Graviton and handle Spot interruptions gracefully, and learn how to apply those practices effectively. Finally, you’ll learn how to combine all three technologies for greater savings across your Amazon EKS environments.

Course level: Advanced

Duration: 1.5 hours

Course outline:

• Amazon EC2 characteristics for containers
• Amazon EKS Auto Mode
• Kubernetes scaling mechanisms
• AWS Graviton processors for containers
• Amazon EC2 Spot Instances for containers
• Demonstration

Get started

Enroll for free in these courses on AWS Skill Builder using the links above and take the first step toward optimizing your AI and container workloads. The courses cover AWS Graviton, AWS Trainium, AWS Inferentia, Amazon EKS Auto Mode, and Amazon EC2 Spot Instances.

Explore more courses and information on AWS chips and Amazon EKS

• AWS Graviton Processors Fundamentals
• Migrating to AWS Graviton
• Optimization with AWS Graviton
• AWS AI Chips – Trainium and Inferentia Fundamentals
• Generative AI Workloads on AWS AI Chips – Trainium and Inferentia
• AWS Graviton Processors
• AWS Trainium
• AWS Inferentia
• Amazon EKS

Special thanks to our subject matter experts for their collaboration, technical expertise, and valuable feedback in developing these courses: Kinnar Kumar Sen, Robert Northard, Christian Melendez, Art Baudo, Rachel Zheng, Markus Adhiwiyogo