Machine Learning in the Real World

Author:

Kampakis Stylianos1

Affiliation:

1. Centre for Blockchain Technologies, University College London, UK

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.

Publisher

IGI Global

Reference24 articles.

1. Amazon Web Services. (n.d.). What is DevOps? Amazon Web Services, Inc. https://aws.amazon.com/devops/what-is-devops/

2. Competence Center Corporate Data Quality. (2020, September 11). Data Strategy to Manage Data Successfully. Competence Center Corporate Data Quality. https://www.cc-cdq.ch/data-strategy

3. DalleMule, L., & Davenport, T. H. (2017, May 1). What’s Your Data Strategy? Harvard Business Review. https://hbr.org/2017/05/whats-your-data-strategy

4. A decade of agile methodologies: Towards explaining agile software development

5. Fowler, M. (2006, May 1). Continuous Integration. https://martinfowler.com/articles/continuousIntegration.html

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