MLOps: Revolutionizing AI Development and Deployment

Author(s)	Manpreet Singh Sachdeva
Country	United States
Abstract	Machine Learning Operations (MLOps) has emerged as a critical discipline in the field of Artificial Intelligence (AI), addressing key challenges in scaling and deploying AI models. This article explores the architecture, best practices, and societal impact of MLOps, highlighting its role in accelerating innovation, enhancing economic productivity, and fostering responsible AI development. We delve into the key components of MLOps, including data engineering, model development, CI/CD pipelines, and governance. The article also examines best practices such as modular pipelines, automated testing, and ethical AI considerations. Despite its transformative potential, MLOps faces challenges including talent shortages, interoperability issues, regulatory compliance, and environmental concerns. By addressing these challenges, MLOps is poised to play a crucial role in shaping the future of AI-driven innovation across industries.
Keywords	Keywords: MLOps, Artificial Intelligence, Machine Learning, DevOps, Data Science
Field	Computer
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-18
Cite This	MLOps: Revolutionizing AI Development and Deployment - Manpreet Singh Sachdeva - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.28794
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.28794
Short DOI	https://doi.org/g8np93

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International Journal For Multidisciplinary Research