Amazon Elastic Kubernetes Service (Amazon EKS) has become a popular choice for customers looking to run their workloads in the Amazon Web Services (AWS) Cloud with customers increasingly choosing to run their AI and Machine Learning (AI/ML) workloads on Amazon EKS. Customers can use Amazon EKS to customize configuration to match their workload requirements. Furthermore, Platform teams can use it to transfer their existing container orchestration model and expertise when deploying new workloads and standardize on Amazon EKS. Kubernetes also provides access to a rich environment of popular open source AI/ML frameworks, tools, and inference engines such as Ray, vLLM, Triton, PyTorch. Lastly, they can use Kubernetes’ tested capability to auto-scale, deploy and manage containerized workloads at scale, and implement the full cluster automation capabilities of EKS.
Some use cases of AI/ML workloads deployed on Amazon EKS include generative AI Model training for Large…
