# Data science and Machine learning in the Clouds: A Perspective for the Future

arXiv: https://arxiv.org/abs/2109.01661 · HTML: https://mlsanity.com/paper/2109.01661/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 172 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [MLCapsule: Guarded Offline Deployment of Machine Learning as a Service](https://mlsanity.com/paper/1808.00590/) (2018)
- [Quantum Perceptron Models](https://mlsanity.com/paper/1602.04799/) (2016)
- [A Comparative Measurement Study of Deep Learning as a Service Framework](https://mlsanity.com/paper/1810.12210/) (2018)
- [GPU coprocessors as a service for deep learning inference in high energy physics](https://mlsanity.com/paper/2007.10359/) (2020)
- [Data Science: A Comprehensive Overview](https://mlsanity.com/paper/2007.03606/) (2020)
- [VeriDL: Integrity Verification of Outsourced Deep Learning Services (Extended Version)](https://mlsanity.com/paper/2107.00495/) (2021)
- [Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service](https://mlsanity.com/paper/1709.07493/) (2017)
- [AXNet: ApproXimate computing using an end-to-end trainable neural network](https://mlsanity.com/paper/1807.10458/) (2018)
- [Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage](https://mlsanity.com/paper/2108.06322/) (2021)
- [Recent Developments in Cloud Based Systems: State of Art](https://mlsanity.com/paper/1501.01323/) (2015)
- [Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions](https://mlsanity.com/paper/1510.06486/) (2015)
- [Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud](https://mlsanity.com/paper/2104.05765/) (2021)

## Built on by (most cited first, 2 of 2)

- [Private and Reliable Neural Network Inference](https://mlsanity.com/paper/2210.15614/) (2022)
- [SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models](https://mlsanity.com/paper/2502.00847/) (2025)

## 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.