Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments

TL;DR

This paper introduces a method for rapidly deploying secure, ephemeral AI workloads in data mesh environments using immutable container OS and infrastructure-as-code, enabling flexible, cost-effective data platform provisioning.

Contribution

It presents a novel, portable approach for creating short-lived Kubernetes clusters across various environments, enhancing interoperability and governance in data mesh architectures.

Findings

Supports rapid deployment of ephemeral AI workloads

Ensures vendor-neutral and portable infrastructure setup

Reduces costs compared to traditional PaaS solutions

Abstract

Many large enterprises that operate highly governed and complex ICT environments have no efficient and effective way to support their Data and AI teams in rapidly spinning up and tearing down self-service data and compute infrastructure, to experiment with new data analytic tools, and deploy data products into operational use. This paper proposes a key piece of the solution to the overall problem, in the form of an on-demand self-service data-platform infrastructure to empower de-centralised data teams to build data products on top of centralised templates, policies and governance. The core innovation is an efficient method to leverage immutable container operating systems and infrastructure-as-code methodologies for creating, from scratch, vendor-neutral and short-lived Kubernetes clusters on-premises and in any cloud environment. Our proposed approach can serve as a repeatable, portable and cost-efficient alternative or complement to commercial Platform-as-a-Service (PaaS) offerings, and this is particularly important in supporting interoperability in complex data mesh environments with a mix of modern and legacy compute infrastructure.