Machine learning in AI Factories – five theses for developing, managing and maintaining data-driven artificial intelligence at large scale

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Wolfgang Hildesheim, Taras Holoyad, Thomas Schmid
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引用次数: 0

Abstract

Abstract The use of artificial intelligence (AI) is today’s dominating technological trend across all industries. With the maturing of deep learning and other data-driven techniques, AI has over the last decade become an essential component for an increasing number of products and services. In parallel to this development, technological advances have been accelerating the production of novel AI models from large-scale datasets. This global phenomenon has been driving the need for an efficient industrialized approach to develop, manage and maintain AI models at large scale. Such an approach is provided by the state-of-the-art operational concept termed AI Factory, which refers to an infrastructure for AI models and implements the idea of AI as a Service (AIaaS). Moreover, it ensures performance, transparency and reproducibility of AI models at any point in the continuous AI development process. This concept, however, does not only require new technologies and architectures, but also new job roles. Here, we discuss current trends, outline requirements and identify success factors for AI Factories. We conclude with recommendations for their successful use in practice as well as perspectives on future developments.
人工智能工厂中的机器学习——关于大规模开发、管理和维护数据驱动的人工智能的五篇论文
人工智能(AI)的使用是当今所有行业的主导技术趋势。随着深度学习和其他数据驱动技术的成熟,人工智能在过去十年中已成为越来越多产品和服务的重要组成部分。与此同时,技术进步也在加速从大规模数据集中产生新的人工智能模型。这一全球现象推动了对高效工业化方法的需求,以大规模开发、管理和维护人工智能模型。这种方法是由称为AI工厂的最先进的操作概念提供的,它指的是AI模型的基础设施,并实现了AI即服务(AIaaS)的思想。此外,它确保了人工智能模型在持续的人工智能开发过程中的任何一点的性能、透明度和可重复性。然而,这个概念不仅需要新的技术和架构,还需要新的工作角色。在这里,我们讨论了当前的趋势,概述了需求并确定了人工智能工厂的成功因素。最后,我们对它们在实践中的成功应用提出了建议,并对未来的发展提出了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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