Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod

This post is co-written with Zhanghao Wu, co-creator of SkyPilot.

The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remains high. Organizations need infrastructure solutions that are not only powerful but also flexible, resilient, and straightforward to manage.

SkyPilot is an open source framework that simplifies running ML workloads by providing a unified abstraction layer that helps ML engineers run their workloads on different compute resources without managing underlying infrastructure complexities. It offers a simple, high-level interface for provisioning resources, scheduling jobs, and managing distributed training across multiple nodes.

Amazon SageMaker HyperPod is a purpose-built infrastructure to develop and deploy large-scale FMs. SageMaker HyperPod not only provides the flexibility to create and use your own software stack, but also provides ...