Vol. 4 No. 1 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Container-Native Data Management for AI Workloads in Amazon EKS

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  • Babulal Shaik
Babulal Shaik
Cloud Solutions Architect at Amazon Web Services, USA
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Published 28-05-2024

Keywords

  • Container-native,
  • AI workloads
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Babulal Shaik, “Container-Native Data Management for AI Workloads in Amazon EKS ”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 206–226, May 2024, Accessed: Dec. 30, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/258

Abstract

As artificial intelligence (AI) continues to evolve, the demand for scalable, efficient, and high-performing data management solutions has become increasingly critical. Containerized environments, especially Kubernetes and Amazon Elastic Kubernetes Service (EKS) have emerged as powerful platforms for managing AI workloads. However, the complexity of AI workloads, which often require vast amounts of data to be processed & stored, presents unique challenges regarding data management. Container-native data management is pivotal in optimizing how data is handled within these environments. It ensures that AI workloads running on Amazon EKS are efficient and capable of meeting the high demands of AI applications. This includes overcoming challenges related to storage architecture, such as choosing between object storage, block storage, and file systems, each offering different trade-offs regarding performance, cost, and ease of access. In addition to storage concerns, data consistency and real-time processing are critical for AI workloads, which rely on fast and reliable access to data. Kubernetes & EKS provide the flexibility to manage distributed data systems, but this requires careful attention to how data is partitioned, replicated, and synchronized across clusters. Scalability is another essential factor—AI workloads can proliferate in data volume, so container-native solutions must scale without compromising performance. The best practices for managing AI workloads in Kubernetes environments involve implementing strategies such as automated scaling, distributed data management, and efficient storage backends that are well-suited to AI applications. These practices ensure that data is always available, accessible, and processed at the speed AI models require, from training to inference. By understanding the intricacies of container-native data management on Amazon EKS, developers and IT, architects can design systems that meet today’s AI-driven applications and remain flexible and scalable for future advancements in AI technologies.

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