Mastering Containers on AWS: Master Amazon ECS, Amazon EKS, Kubernetes, Karpenter, Service Meshes, Observability, and AI Workloads on AWS

Key Features
● Get a free one-month digital subscription to http://www.avaskillshelf.com.
● Production-grade ECS and EKS coverage from cluster provisioning and networking to autoscaling, security, and observability.
● Hands-on Kubernetes engineering with Karpenter, KEDA, service meshes, IAM, and multi-environment CI/CD pipeline design.
● AI and ML workload deployment on EKS covering GPU scheduling, AWS Trainium, Inferentia, and inference pipeline architecture.

Book Description

Running Containers Is Easy. Operating Platforms at Scale Is Engineering.

Running containers in production on AWS demands more than knowing the commands — it requires the architectural depth to design systems that scale securely under real load. Mastering Containers on AWSis a practitioner’s guide to Amazon ECS and Amazon EKS written by an AWS Ambassador and Containers Community Builder, covering production-grade patterns used by real engineering teams from first principles to enterprise-scale deployment.

You begin with container and networking fundamentals, then advance through cluster provisioning, IAM and secrets management, autoscaling with Karpenter, service meshes, CI/CD pipelines, cost optimization, and observability. Every chapter is grounded in real engineering scenarios — how systems fail, how to debug them, and how to build architectures that actually hold up under production conditions.

The final section covers AI and ML workloads on EKS, including GPU scheduling, AWS Trainium, Inferentia, and inference pipeline design. So, by the end of the book, you will have a complete mental model of containerisation on AWS, and the hands-on patterns to back it up.

What you will learn
● Deploy and manage production-grade ECS and EKS workloads on AWS with confidence.
● Design secure container networking using VPCs, service meshes, and IAM across environments.
● Autoscale Kubernetes clusters efficiently using Karpenter and KEDA for production workloads.
● Build multi-environment CI/CD and GitOps pipelines for containerised applications on AWS.
● Optimize container costs and observe workloads using AWS-native monitoring and troubleshooting tooling.
● Run AI and ML inference workloads on EKS using GPUs, AWS Trainium, and Inferentia.

Table of Contents
1. Foundations of Containers and Cloud-Native on AWS
2. Docker and Container Fundamentals
3. AWS Networking and Security Basics for Containers
4. Running Containers with Amazon ECS
5. Running Containers with Amazon EKS
6. Serverless Containers with AWS Fargate and App Runner
7. CI/CD and GitOps for Containerized Workloads
8. Observability, Monitoring, and Troubleshooting
9. Security, Compliance, and Governance for Container Platforms
10. Architecting Production-Grade Container Platforms on AWS
11. Architecting AI/ML Workloads on Amazon EKS
12. Migration Strategies and Application Modernization on Amazon EKS
13. Containers at the Edge with AWS
14. Cost Optimization and Future Directions
Index