The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning in the Real World
|
Author(s): Stylianos Kampakis (Centre for Blockchain Technologies, University College London, UK)
Copyright: 2023
Pages: 16
Source title:
Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch104
Purchase
|
Abstract
Most data scientists and machine learning practitioners focus on algorithm development and implementation. However, the proper and successful application of data science in an organisation cannot be separated from business objectives and organisational dynamics. This way of thinking, however, can feel foreign to many data scientists who focus mostly on technical details. The goal of this article is to outline some of the considerations that a data scientist needs to take into account when implementing data science within an organisation. More specifically, this article discusses the topics of data strategy, data science processes, and some recent developments like MLOps.
Related Content
Princy Pappachan, Sreerakuvandana, Mosiur Rahaman.
© 2024.
26 pages.
|
Winfred Yaokumah, Charity Y. M. Baidoo, Ebenezer Owusu.
© 2024.
23 pages.
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Francesco Marongiu, Domenico Santaniello.
© 2024.
25 pages.
|
Suchismita Satapathy.
© 2024.
19 pages.
|
Xinyi Gao, Minh Nguyen, Wei Qi Yan.
© 2024.
13 pages.
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Angelo Lorusso, Domenico Santaniello, Carmine Valentino.
© 2024.
30 pages.
|
Pratyay Das, Amit Kumar Shankar, Ahona Ghosh, Sriparna Saha.
© 2024.
32 pages.
|
|
|