Data science and Machine learning in the Clouds: A Perspective for the Future
Not on the map. Why?
Abstract (arXiv)
As we are fast approaching the beginning of a paradigm shift in the field of science, Data driven science (the so called fourth science paradigm) is going to be the driving force in research and innovation. From medicine to biodiversity and astronomy to geology, all these terms are somehow going to be affected by this paradigm shift. The huge amount of data to be processed under this new paradigm will be a major concern in the future and one will strongly require cloud based services in all the aspects of these computations (from storage to compute and other services). Another aspect will be energy consumption and performance of prediction jobs and tasks within such a scientific paradigm which will change the way one sees computation. Data science has heavily impacted or rather triggered the emergence of Machine Learning, Signal/Image/Video processing related algorithms, Artificial intelligence, Robotics, health informatics, geoinformatics, and many more such areas of interest. Hence, we envisage an era where Data science can deliver its promises with the help of the existing cloud based platforms and services with the addition of new services. In this article, we discuss about data driven science and Machine learning and how they are going to be linked through cloud based services in the future. It also discusses the rise of paradigms like approximate computing, quantum computing and many more in recent times and their applicability in big data processing, data science, analytics, prediction and machine learning in the cloud environments.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 172 references that are in the map. First posted on arXiv 2 Sep 2021.
Builds on
13 of 172 references.
References · 13
- 2018Supervised learning with quantum enhanced feature spacescited by 654
- 2018MLCapsule: Guarded Offline Deployment of Machine Learning as a Servicecited by 29
- 2016Quantum Perceptron Modelscited by 27
- 2018A Comparative Measurement Study of Deep Learning as a Service Frameworkcited by 10
- 2020GPU coprocessors as a service for deep learning inference in high energy physicscited by 9
- 2020Data Science: A Comprehensive Overviewcited by 6
- 2021VeriDL: Integrity Verification of Outsourced Deep Learning Services (Extended Version)cited by 4
- 2017Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Servicecited by 3
- 2018AXNet: ApproXimate computing using an end-to-end trainable neural networkcited by 2
- 2021Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storagecited by 2
- 2015Recent Developments in Cloud Based Systems: State of Artcited by 1
- 2015Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directionscited by 1
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.