Abstract
The relatively short timeframe of the data-oriented approach has made cloud architecture the basis for flexible and effective data analysis and data science projects. This paper presents the design strategies and considerations of cloud architectures for data science platforms that compliments modern analytics and machine learning workloads. Sub-processes like data acquisition, management, analysis, and coordination are discussed, as well as their part in supporting moment and science driven decision-making. Responsiveness is given on the use of tools and platforms that are built natively on cloud to help in getting better collaboration, better costs and high performance. Security and compliance issues are discussed in order to create a viable base for the protection of potentially private and confidential information in risky business sectors. Also, there are emerging trends like edge computing and AI analytics that describe in detail what may be expected in cloud computing for data science in the future. In the framework of this paper, the best practices and case examples serve as recommendations for developing and improving cloud architecture for innovative data capabilities within an organization.
Published Version
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