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Use of Machine Learning Services in Cloud

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Computer Networks, Big Data and IoT

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 66))

Abstract

Machine learning services are the comprehensive description of integrated and semiautomated web devices covering most facilities problems such as preprocessing information, design preparation, and design assessment, with the further forecast. REST APIs can bridge the outcomes of predictions with one’s inner IT infrastructure. Like the original SaaS, IaaS, and PaaS cloud delivery models, ML and AI fields cover high-level services to provide infrastructure and platform, exposed as APIs. This article identifying the most used Cloud Technologies for Machine Learning as a Service (MLaaS): Google Cloud AI, Amazon, and Microsoft Azure.

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Authors and Affiliations

  1. Faculty of Technology and Engineering (FTE), Devang Patel Institute of Advance Technology and Research-(DEPSTAR), Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, India

    Chandrashekhar S. Pawar, Amit Ganatra, Amit Nayak, Dipak Ramoliya & Rajesh Patel

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  1. Chandrashekhar S. Pawar
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  2. Amit Ganatra
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  3. Amit Nayak
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  4. Dipak Ramoliya
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  5. Rajesh Patel
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Editor information

Editors and Affiliations

  1. Department of CSE, KGiSL Institute of Technology, Coimbatore, India

    A.Pasumpon Pandian

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. Edith Cowan University (ECU), Joondalup, WA, Australia

    Syed Mohammed Shamsul Islam

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pawar, C.S., Ganatra, A., Nayak, A., Ramoliya, D., Patel, R. (2021). Use of Machine Learning Services in Cloud. In: Pandian, A., Fernando, X., Islam, S.M.S. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_5

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  • DOI: https://doi.org/10.1007/978-981-16-0965-7_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0964-0

  • Online ISBN: 978-981-16-0965-7

  • eBook Packages: EngineeringEngineering (R0)

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