The Convergence of DevOps, Data Science, and AI in Software Development - Gopalakrishnan Mahadevan,; Santosh Panendra Bandaru,; Chandra Jaiswal,; Murali Kadiyala,; N V Rama Sai Chalapathi Gupta Lakkimsetty, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
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.
Keywords: DevOps; Machine Learning; Continuous Integration