Year: 2025
Type: article
Abstract: This research paper explores the transformative convergence of DevOps, Data Science, and Artificial Intelligence (AI) in modern software development, culminating in the unified operational paradigm of Platform Ops. Through a comprehensive review of scholarly literature, industry reports, and real-world case studies, the study proposes that traditional development models fall short in addressing the complexities of today's data-driven, rapidly evolving environments. The integration of MLOps (Machine Learning Operations) and DataOps (Data Operations) with DevOps enhances the software development lifecycle by enabling continuous integration, real-time data validation, automated model deployment, and predictive analytics. The paper introduces Platform Ops as a cohesive framework that orchestrates these three domains, providing a scalable and intelligent foundation for managing modern, AI-enabled systems. Key benefits include improved software quality, accelerated delivery timelines, and enhanced decision-making capabilities. However, the paper also highlights significant implementation challenges, particularly the need for cultural transformation, cross-functional collaboration, and robust governance structures. The study offers a strategic roadmap for organizations—especially in consulting and retail sectors—to adopt this convergence model, thereby optimizing digital innovation, operational efficiency, and business responsiveness in an increasingly competitive landscape.
Authors: Gopalakrishnan Mahadevan, Santosh Panendra Bandaru, Chandra Jaiswal, Murali Kadiyala, N V Rama Sai Chalapathi Gupta Lakkimsetty
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414073312