Developing an AI Model Registry and Lifecycle Management System for Cross-Functional Tech Teams

This paper presents a comprehensive solution for managing AI models across their lifecycle through the development of an AI model registry and lifecycle management system. As AI continues to play a crucial role across industries, the complexity of managing models—from development to deployment—presents significant challenges, especially within cross-functional teams. These challenges include issues such as model versioning, metadata management, deployment inconsistencies, and communication breakdowns among data scientists, engineers, and business stakeholders. The proposed system addresses these challenges by providing a centralized platform that integrates features such as version control, metadata management, and automated deployment, thereby improving transparency and reducing the risk of deployment errors. Furthermore, the system fosters enhanced collaboration by integrating widely-used project management tools like GitHub, Jira, and Slack, ensuring that teams remain aligned throughout the model's lifecycle. By enabling continuous monitoring and incorporating automated model drift detection, the system ensures that AI models remain accurate and efficient post-deployment. This paper also explores the technical implementation strategy for the system, including the use of containerization, cloud-native infrastructure, and microservices architecture to ensure scalability and flexibility. The implications of this work extend beyond technical considerations, as it enhances collaboration, improves model quality, and accelerates deployment cycles. Future research directions include exploring automation in model updates, scalability in large enterprises, and the integration of additional tools and frameworks. This work provides a critical step toward optimizing AI model management, offering a scalable, efficient, and secure approach to managing AI models throughout their lifecycle.