# Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service

arXiv: https://arxiv.org/abs/1709.07493 · HTML: https://mlsanity.com/paper/1709.07493/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 3 of 3)

- [Data science and Machine learning in the Clouds: A Perspective for the Future](https://mlsanity.com/paper/2109.01661/) (2021)
- [HPTMT Parallel Operators for High Performance Data Science & Data Engineering](https://mlsanity.com/paper/2108.06001/) (2021)
- [Position Paper on Dataset Engineering to Accelerate Science](https://mlsanity.com/paper/2303.05545/) (2023)

## Abstract (arXiv)

Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data. The intersection of these trends is what is called, nowadays, as Big Data Science. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting big data science as a commodity service for data scientists. We provide various insights about the latest ongoing developments and open challenges in this domain.