为数据网格环境提供安全瞬时的AI计算集群，支持快速部署与销毁。
Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments
- 基于不可变容器系统和代码化基础设施，按需创建临时K8s集群。
- 可在本地或任意云环境部署，支持混合架构互操作。
- 适合需要快速实验、自主运维的数据团队使用。
许多大型企业缺乏高效手段支持数据与AI团队快速搭建和拆除自助式数据与计算基础设施，以试验新分析工具或将数据产品投入生产。本文提出解决方案的核心部分：一种按需自服务的数据平台基础设施，使分散的数据团队可基于集中模板、策略与治理构建数据产品。核心创新在于利用不可变容器操作系统和基础设施即代码方法，从零开始在本地或任意云环境中高效创建与厂商无关、短期运行的Kubernetes集群。该方案可作为商业PaaS服务的可重复、可移植、低成本替代或补充，尤其适用于融合现代与传统计算基础设施的复杂数据网格环境。
原文摘要 · Abstract (English)
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.
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