Amazon has introduced new features on its SageMaker HyperPod platform, a significant advancement in optimizing the training and inference of models at scale in the field of machine learning. This infrastructure is designed to mitigate the undifferentiated workloads that often come with creating and optimizing environments necessary for artificial intelligence. With its increasing implementation in different sectors, it becomes essential to have robust security options and storage solutions that meet the demands of large companies and their internal policies.
Among the innovations implemented in HyperPod EKS are features that offer greater control and flexibility in workload management. The first of these is support for Customer Managed Keys (CMK), which allows users to encrypt the storage volumes associated with HyperPod instances using their own keys. This option is particularly crucial for industries that must comply with strict regulations, such as HIPAA and FIPS, thus supporting data security.
Additionally, the platform now supports the Amazon Elastic Block Store (EBS) Container Storage Interface (CSI) driver, which efficiently manages the lifecycle of EBS volumes used as storage for Kubernetes volumes. This enhancement facilitates dynamic storage management, optimizing the handling of massive datasets typically used in training and inference of AI models.
The inclusion of CMK not only provides meticulous control over encryption, but also ensures regulatory compliance and security governance, protecting both Amazon EBS volumes and custom machine images used in HyperPod. These innovations make SageMaker HyperPod an even more robust option for organizations looking to develop and deploy AI models securely and efficiently. The combination of EBS volumes and support for customer-managed keys sets a high standard in security and regulatory compliance, allowing companies to maximize the potential of artificial intelligence in their operations.
Referrer: MiMub in Spanish
