Using GAS for Speedy Generation of Hybrid Multi-Cloud Auto Generated AI Services
Using GAS for Speedy Generation of Hybrid Multi-Cloud Auto Generated AI Services
复制标题
使用 GAS 快速生成混合多云自动生成的 AI 服务
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Wooseok Chang
中科院分区：
文献类型：
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作者：
G. Laszewski;Anthony Orlowski;Richard H. Otten;Reilly Markowitz;Sunny G. Gandhi;Adam Chai;G. Fox;Wooseok Chang
当今的任务需要执行大量的分析任务，以应对社会中影响许多领域的最先进的计算挑战，包括医疗保健，汽车，银行，自然语言处理，图像检测以及更多与数据分析相关的任务。共享现有的分析功能允许重用并减少总体工作量。然而，在云计算时代集成部署框架对于领域专家来说往往是遥不可及的。需要简单的框架，允许非专家在云中部署和托管服务。为了避免供应商锁定，我们需要一个通用的可组合分析服务框架，允许用户集成他们的服务和云中提供的服务，不仅是一个，而是许多云计算和服务提供商。我们报告了我们进行的工作，以提供一个服务集成框架，用于在多云提供商上组成广义分析框架，我们称之为广义AI服务（GAS）生成器。我们通过在各种云提供商（包括AWS、Azure和Google）和边缘计算物联网设备上展示有用的分析工作流来展示框架的可用性。这些例子都是基于Scikit的，也可以在教育环境中使用，可以很容易地复制和扩展。基准被用来比较不同的服务和展示一般的可复制性。
—Today’s tasks require a plethora of analytics tasks to be conducted to tackle state-of-the-art computational challenges posed in society impacting many areas including health care, automotive, banking, natural language processing, image detection, and many more data analytics related tasks. Sharing existing analytics functions allows reuse and reduces overall effort. However, integrating deployment frameworks in the age of cloud computing is often out of reach for domain experts. Simple frameworks are needed that allow even non-experts to deploy and host services in the cloud. To avoid vendor lock-in, we require a generalized composable analytics service framework that allows users to integrate their services and those offered in clouds, not only by one, but by many cloud compute and service providers. Wereport on work that we conducted to provide a service integration framework for composing generalized analytics frameworks on multi-cloud providers that we call our Generalized AI Service (GAS) Generator. We demonstrate the framework’s usability by showcasing useful analytics workflows on various cloud providers, including AWS, Azure, and Google and edge computing IoT devices. The examples are based on Scikit learn to use them also in educational settings that can easily be replicated and expanded upon. Benchmarks are used to compare the different services and showcase general replicability.