Lilly Tech Systems
Learn AWS EKS for ML
Master running machine learning workloads on Amazon Elastic Kubernetes Service. From cluster setup and GPU node management to Kubeflow pipelines and scalable model serving.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why Kubernetes for ML? Understand the benefits of EKS for training, serving, and managing ML workloads at scale.
2. Cluster Setup
Create and configure an EKS cluster optimized for ML using eksctl, Terraform, or the AWS console.
3. GPU Nodes
Configure GPU node groups, install NVIDIA device plugins, manage GPU scheduling, and use Karpenter for auto-scaling.
4. Kubeflow
Deploy Kubeflow on EKS for ML pipelines, notebook servers, experiment tracking, and hyperparameter tuning.
5. Model Serving
Serve models with KServe, Triton Inference Server, and TorchServe on EKS with auto-scaling and canary deployments.
6. Best Practices
Security, cost optimization, multi-tenancy, monitoring, and production-readiness for ML on EKS.
What You'll Learn
By the end of this course, you'll be able to:
Build ML Clusters
Set up production-grade EKS clusters with GPU node groups, auto-scaling, and proper networking for ML workloads.
Run ML Pipelines
Deploy and manage Kubeflow for end-to-end ML pipelines including training, evaluation, and deployment.
Serve Models
Deploy models at scale using KServe and Triton with auto-scaling, A/B testing, and monitoring.
Optimize Operations
Implement cost controls, security policies, and operational best practices for ML on Kubernetes.