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Scaling AI responsibly: Leveraging MLOps for sustainable machine learning deployments
Independent Researcher, Aldie, Virginia, USA.
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 3447-3455.
Article DOI: 10.30574/ijsra.2024.13.1.1798
Publication history:
Received on 14 August 2024; revised on 22 October 2024; accepted on 25 October 2024
Abstract:
As artificial intelligence (AI) has now become an integral part of many industries and amended their business processes, we have seen both technologies pushing innovation forward and at the same time posing some severe scaling issues with resource utilization and distributing access ethically. One can learn to execute AI systems at scale while optimizing performance, governance, and possibly sustainability simultaneously. In this article, Machine Learning Operations (MLOps) is investigated as a means to support sustainable (and ethical) scalability of AI through workflow streamlining, resource utilization optimization, and the (re)introduction of governance. A final view of the AI scalability challenges shows any unregulated growth pollutes the air with carbon emissions and even poses questions about learning ethics: it continues is grow with considerable bias already. To the best of our knowledge, this work presents an energy-efficient, carbon footprint quantified, and fair MLOps framework. Finally, we discuss how surrounding trends in AI scalability, such as federated learning, green AI, and explainable AI could help to apply these trends. In this article, we show how to use MLOps practices to sustainability goals as actionable insights for stakeholders like organizations, researchers, and policymakers as you scale responsible AI. It argues that sustainable MLOps is not about creating techno optimality while saving the planet but an active social obligation to make sure that the gains of using AI do not come at the expense of the rest of the world or at the cost of harming.
Keywords:
MLOps (Machine Learning Operations); Sustainability; scalability; Responsible AI; Ethical AI; Resource optimization; Environmental impact; Bias mitigation; Governance framework
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
