Uma investigação dos desafios no ciclo de vida do aprendizado de máquina e a importância do MLOps: um survey
Year: 2022
Type: dissertation
Study design: Observational Study
Abstract: Quite recently, considerable attention has been paid to developing artificial intelligence and data science areas. This has been driven by scientific advances and the growing number of software and services that are popularizing machine learning techniques and algorithms and driving people with less knowledge in areas such as statistics and mathematics to create their predictive models. As a result, the machine learning field is no longer only scientific and has aroused the interest of companies from different domains. These events led to the emergence of multiple tools such as Scikit-Learn, Tensorflow, Keras, Pycaret, and a vast number of cloud-based machine learning services that provide an acceleration in the development of predictive models at speeds never seen. However, many challenges remain in operationalizing and maintaining machine learning-centered products, making many business initiatives frustrated. In this scenario, practical experience shows that machine learning is only a slice of a more extensive set of practices and technologies necessary to build solutions in this area. In this paper, the main goal is to identify the challenges currently faced by data scientists in developing Machine Learning-centric products and how Machine Learning Operations can support overcoming them. For this purpose, a survey was conducted that collected answers from 66 Brazilian professionals in data science. From the challenges identified, the importance of Machine Learning Operations practices as an integrated part of the Machine Learning lifecycle was explored. Finally, this work contributes to filling the gap in Machine Learning Operations in daily activities involving data science and advancing this research field in Brazil.
Author: Amorim, Bruno Faustino
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7034682796