Convergence of Architecture and Machine Learning Product Development Life Cycle

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has enabled organizations to innovate and gain a competitive edge through data-driven products. However, transitioning ML projects from prototypes to scalable, production-grade IT systems remains a challenge for organizations that fail to adopt sound enterprise IT architecture practices. This paper investigates the key inhibitors to scaling ML products, with a particular emphasis on the critical role of enterprise IT architecture. We propose a comprehensive Machine Learning Product Development Lifecycle (ML-PDLC) framework that aligns organizational structures, DevTestSecOps principles, and governance processes to enable sustainable AI adoption. Additionally, we explore the emerging field of Agentic AI-autonomous, goal-driven systems-and its implications for the future management of the ML lifecycle. We aim to guide enterprises in building secure, scalable, and maintainable ML products that deliver measurable business value.