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

TL;DR

This paper discusses the integration of big data systems with machine learning, emphasizing cloud computing as a key enabler for scalable data science services and analyzing current developments and challenges.

Contribution

It provides a detailed analysis of the software stack for delivering big data science as a cloud-based service, highlighting recent advancements and open issues.

Findings

Cloud computing enables scalable big data analytics as a service.

The paper identifies key challenges in deploying big data science solutions.

Insights into the latest developments in big data and machine learning integration.

Abstract

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.