撰写者：高级研究助理杰里米·科恩（Jeremy Korn）和研究副总裁尼克·帕特列肯（Nick Patiencemany）组织为AI和机器学习申请的需求做好了准备，但他们准备花钱来改变这种情况。这些只是我们可以从企业的新声音中得出的几个结论：AI和机器学习基础设施2019调查。几乎一半（45％）的企业表明他们当前的AI基础架构将无法满足未来的需求（见图1），这提示了一些问题：•为什么是？•他们建议对此做什么？他们准备花钱解决问题吗？图1是为什么？从广义上讲，数据是基础架构需要大规模进行大规模提供AI的原因，我们调查中有89％的受访者说他们期望数据量的数据量使用机器学习工作负载在明年增加，几乎一半的预计增加了25％或更多。大部分增长将来自非结构化数据，因为AI和机器学习的最具变色性用例涉及从非结构化数据中获得见解，无论是文本，图像，音频还是视频。他们建议对此做什么？组织了解，对于他们来说，要大规模利用AI，这不仅仅是扩展现有基础架构的情况。需要新的基础架构来应对机器学习工作负载的需求，包括新的可扩展存储，专用的加速器和低延迟网络。 These need to be deployed across a variety of execution venues.Enterprises also express a variety of concerns about their AI infrastructures, from the security of these systems to the opacity of data management capabilities. Overhauling AI infrastructure demands more than just buying better hardware; it will require new tools and updates to architectural paradigms.Are they prepared to spend money to fix the problem?Yes, they are. Our survey shows that 83% of responding enterprises say they will expand AI infrastructure budgets next year, with 39% of those projecting an increase of 25% or more. Spending on cloud-based AI platforms will lead the charge, with 89% of respondents planning to increase spending on them in the next year. Our Voice of the Enterprise: AI and Machine Learning Infrastructure 2019 survey contains a lot more data on subjects such as spending decision-makers, the specific points in the machine learning process that put strain on infrastructure, the types of AI-specific infrastructure components organizations are looking to buy, the areas in which skill shortages are most acute, and how often and where machine learning models are trained and deployed. For more insight, check out this free Market Insight report.