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Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 8, Special Issue 1 (2019)
Examination of the role of auto ML in democratizing machine learning
Author(s):
Virat Saxena, Piyush Kumar Rai and Arvind Kumar
Virat Saxena, Piyush Kumar Rai and Arvind Kumar
Abstract:
The democratization of Machine Learning (ML) has emerged as a pivotal paradigm shift, aiming to make ML tools and techniques accessible to a broader audience beyond data scientists and experts. This review paper delves into the transformative role of Automated Machine Learning (AutoML) in realizing this vision. AutoML, as an innovative approach, streamlines the ML pipeline, automating various stages such as data preprocessing, model selection, and hyperparameter tuning. By reducing the barriers to entry and mitigating the technical complexities associated with ML, AutoML holds the potential to democratize ML expertise.
The democratization of Machine Learning (ML) has emerged as a pivotal paradigm shift, aiming to make ML tools and techniques accessible to a broader audience beyond data scientists and experts. This review paper delves into the transformative role of Automated Machine Learning (AutoML) in realizing this vision. AutoML, as an innovative approach, streamlines the ML pipeline, automating various stages such as data preprocessing, model selection, and hyperparameter tuning. By reducing the barriers to entry and mitigating the technical complexities associated with ML, AutoML holds the potential to democratize ML expertise.
The paper commences with a comprehensive exploration of the foundational concepts underpinning AutoML, elucidating its mechanisms and methodologies. Through a meticulous analysis of recent advancements, the review delineates the evolution of AutoML frameworks and tools. Keywords such as "automated model selection," "hyperparameter optimization," and "algorithmic transparency" weave through the narrative, highlighting the multifaceted capabilities of AutoML in addressing intricate challenges encountered in traditional ML workflows.
A key focal point of this review is the examination of how AutoML democratizes ML by fostering inclusivity. The discussion revolves around the empowerment of non-experts, enabling them to harness the potential of ML without an in-depth understanding of its intricacies. The elucidation of user-friendly interfaces and intuitive platforms facilitates the integration of AutoML into diverse domains, democratizing access to ML capabilities across industries.
Furthermore, the paper assesses the impact of AutoML in accelerating model development and deployment. By automating time-consuming tasks and minimizing human intervention, AutoML not only expedites the ML lifecycle but also enhances the reproducibility of models. The incorporation of "reproducibility" and "scalability" as integral keywords underscores the importance of AutoML in fostering a sustainable ML ecosystem.
In exploring the democratization of ML through AutoML, the review also navigates ethical considerations and challenges. Keywords like "fairness," "interpretability," and "bias mitigation" are intricately woven into the discourse, reflecting the need for responsible and ethical deployment of AutoML systems.
Pages: 29-33 | 182 Views 95 Downloads
How to cite this article:
Virat Saxena, Piyush Kumar Rai and Arvind Kumar. Examination of the role of auto ML in democratizing machine learning. The Pharma Innovation Journal. 2019; 8(1S): 29-33. DOI: 10.22271/tpi.2019.v8.i1Sa.25239
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
