Driving Business Value with AI: A Framework for MLOps-driven Enterprise Adoption

Balaji Dhamodharan

Abstract


The adoption of Artificial Intelligence (AI) within enterprises has become increasingly crucial for driving business value and gaining a competitive edge in today's digital economy. However, implementing AI at scale presents challenges related to model deployment, monitoring, and management. This paper presents a comprehensive framework for MLOps-driven enterprise adoption, aiming to streamline the end-to-end AI lifecycle and maximize the value derived from AI initiatives. The framework encompasses key components such as infrastructure automation, continuous integration and deployment (CI/CD), model monitoring, governance, and collaboration, providing organizations with a structured approach to operationalizing AI at scale. Through real-world examples and case studies, the paper demonstrates how the MLOps framework enables enterprises to accelerate time-to-market, improve model reliability, and optimize resource allocation, ultimately driving business innovation and growth.


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