Category: Learning Levels

This post demonstrates a real-world example of integrating EKS Hybrid Nodes with NVIDIA DGX Spark, a compact and energy-efficient GPU platform optimized for edge AI deployment. In this post we walk you through deploying a large language model (LLM) for low-latency generative AI inference on-premises, setting up node monitoring and GPU observability with centralized management through Amazon EKS.

In this post, we will walk you through building an automated deployment pipeline using GitHub Actions. You will create a workflow that triggers on code changes, builds Docker images, pushes them to Amazon ECR, and deploys to Amazon ECS Express Mode using IAM roles for secure authentication. By the end, you will have a continuous integration and continuous delivery (CI/CD) workflow that automatically deploys your application when you push code.

This blog post focuses on ACK and kro capabilities. For a deep dive on Amazon EKS capability for Argo CD, check out Deep dive: Streamlining GitOps with Amazon EKS capability for Argo CD.

In this post, we demonstrate a solution that uses Amazon Data Firehose to aggregate logs from the Amazon EKS control plane and data plane, and send them to Amazon Simple Storage Service (Amazon S3). Finally, we use Amazon Q Business and its Amazon S3 connector to synchronize the logs, index the log data in Amazon S3, and enable a chat experience powered by the generative AI capabilities of Amazon Q Business.

In this blog post, you will learn how to implement the elastic high-throughput file system using Amazon Elastic Kubernetes Service (Amazon EKS) and CNCF Fluid, set up efficient data caching mechanisms, and orchestrate training workflows using KubeRay.

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 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 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.

In this deep dive, we explore advanced scenarios with Argo CD including hub-and-spoke multi-cluster deployments, native AWS service integrations, multi-tenancy implementation, scaling with advanced Argo CD configurations and integration with CI/CD pipeline.