Running Al Workloads on Amazon EKS: A scalable, flexible Approach to ML Deployment

---

Introduction: -

As machine leaning and AI continue to evolve, organizations are seeking scalable, reliable, and flexible environment to build, train, and serve models. One solution gaining rapid adoption is Amazon Elastic Kubernets Service(EKS)— a managed Kubernets service that brings the power of container orchestration to the world of AI.

In this post, we will explore why EKS is a powerful choice for AI/ML workloads, what a typical architecture looks like, and how to get started with key tools and best practices.

---

Why Use Amazon EKS for AI Workloads?

Running AI on EKS has several compelling benefits:

• Scalability: Seamlessly scale training and inference across multiple compute nodes, including GPU-backed EC2 instances.

• Flexibility: Bring your own ML stack and customize your environment using open-source tools.

• Portability: Package models into containers and move them easily between environments (on-prem, AWS, hybrid).

• Cost Optimization: Use Spot Instances, autoscaling, and right-sized GPU nodes.

• Integration: Easily connect to Amazon S3, FSx for Lustre, CloudWatch, and even Amazon SageMaker.

---

Architecture for AI on EKS

Let’s walk through a typical high-level setup:

• Amazon S3 / FSx for Lustre: Store large datasets and provide fast access for training.

• GPU-enabled Pods: Use EC2 instances like p3, g4, or g5 for efficient training.

• Inference Services: Run real-time inference using REST APIs on Kubernetes Pods.

• Monitoring: Use Amazon CloudWatch for centralized logging and metrics.

---

Example Use Cases

• Distributed deep learning with TensorFlow + Horovod.

• NLP model serving with Hugging Face Transformers.

• Data preprocessing using Apache Spark on Kubernetes.

• Full ML lifecycle orchestration using Kubeflow or Argo Workflows.

---

Tools That Make It All Work

---

Best Practices for Success

1. Use GPU Nodes Efficiently:

   • Assign taints/tolerations to restrict GPU usage to training pods only.

   • Right-size nodes with EC2 instance types like g5.2xlarge or p4d.24xlarge.

2. Secure Access with IRSA:

   • Leverage IAM Roles for Service Accounts (IRSA) to securely grant Pods access to AWS services.

3. Set Up Auto Scaling:

   • Use Cluster Autoscaler for dynamic node provisioning.

   • Use Horizontal Pod Autoscaler (HPA) for inference services.

4. Monitor Everything:

   • Integrate Prometheus + Grafana for real-time observability.

   • Ship logs and metrics to Amazon CloudWatch.

---

Wrapping Up

In this post, we explored how to train machine learning models on Amazon EKS using PyTorch and GPU-enabled pods — with fast data access from Amazon FSx for Lustre and Amazon S3, and metrics flowing into Amazon CloudWatch.

This setup gives you the flexibility to scale your training workloads efficiently while staying fully container-native.

But training is just half the journey.

---

In the next post, we’ll shift gears and dive into the serving side:

• How do you package and deploy your model?

• How does real-time inference actually run inside Kubernetes?

• And what tools can you use to monitor and optimize it?

---

Stay tuned — we’re going from training pods to production-grade inference services on EKS.

#NextUp: Deploying PyTorch Inference on EKS — Scalable & Real-Time