# Best Practices for Running AI/ML Workloads
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**Tip**  
 [Explore](https://aws-experience.com/emea/smb/events/series/get-hands-on-with-amazon-eks?trk=4a9b4147-2490-4c63-bc9f-f8a84b122c8c&sc_channel=el) best practices through Amazon EKS workshops.

Implementing best practices when running AI/ML workloads on EKS can ensure that those workloads are performant, cost-effective, resilient, and properly resourced. Best practices are divided into the following general sections: Compute, Networking, Storage, Observability, and Performance.

## Feedback
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This guide is being released on GitHub so as to collect direct feedback and suggestions from the broader EKS/Kubernetes community. If you have a best practice that you feel we ought to include in the guide, please file an issue or submit a PR in the GitHub repository. Our intention is to update the guide periodically as new features are added to the service or when a new best practice evolves.