Containers

This blog post dives deeper into optimizing Amazon EKS Auto Mode clusters using AWS Graviton and Amazon EC2 Spot instances. AWS customers adopt AWS Graviton instances to achieve up to 40% higher price-performance ratio and up to 60% less energy to meet their sustainability goals. Additionally, AWS customers use Amazon EC2 Spot instances for eligible workloads to save up to 90% on Amazon Elastic Compute Cloud (Amazon EC2) On-Demand costs.

In this blog post, we show how to create and manage a fleet of Amazon Elastic Kubernetes Service (Amazon EKS) clusters using Kube Resource Orchestrator (kro), AWS Controllers for Kubernetes (ACK), and Argo CD. These tools allow you to implement a GitOps-based cluster management solution to increase productivity and improve consistency and standardization by using the Kubernetes API for end-to-end operations.

In this post, we demonstrate how to capture specific Amazon ECS events using EventBridge rules for enhanced monitoring and troubleshooting of your containerized applications. We show you how to customize EventBridge filtering patterns to capture the specific Amazon ECS events that matter for your troubleshooting and monitoring needs.

In this post, we walk you through building an end-to-end recovery system, showing you how to configure traffic shifting that protects your applications from Availability Zone impairments while maintaining the high availability that your business demands.

In this post we demonstrate how using GitLab Runners on EKS Auto Mode, combined with Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances, can deliver enterprise-scale CI/CD capabilities while achieving up to 90% cost reduction when compared to traditional deployment models. This approach not only optimizes operational expenses, but also provides resilient, scalable pipeline execution.

In this post, we focus on observing and scaling ML operations (MLOps) infrastructure on Kubernetes. MLOps platforms running on Amazon EKS provide powerful built-in capabilities for logging, monitoring, and alerting that are essential for maintaining healthy ML systems at scale.

This post looks at various options for container image caching, model training, and inferencing workloads. This post also discusses various storage options such as Amazon Simple Storage Service (Amazon S3), FSx for lustre, S3 Express One Zone, and Amazon S3 Connector for PyTorch.

This post details how platform engineering teams can build an assurance pipeline for Amazon EKS deployments, incorporating validation frameworks that verify configurations, test infrastructure as code (IaC), assess application resilience, and establish compliance with organizational standards.

Amazon Web Services (AWS) announced the availability of DNS-based and Admin network policies for Amazon Elastic Kubernetes Service (EKS) Auto mode and Admin network policies for both EKS Auto mode and EKS on Amazon Elastic Compute Cloud (EC2), providing enhanced capabilities to secure network traffic both within your clusters and to external endpoints. In this post, we explore practical use cases that demonstrate how these policies solve real-world challenges and remove the need to rely on third-party software across different deployment scenarios, from securing access to external services to hybrid cloud integration and multi-tenant environments.

This post explores using the Amazon CloudWatch monitoring, including new Amazon EKS metrics and the CloudWatch Observability Operator, to gain deeper visibility into cluster operations, detect issues, understand bottlenecks, and maintain healthy EKS clusters.