Continuous Deployment of AI Systems: Strategies for Seamless Updates and Rollbacks

Authors: Swamy Prasadarao Velaga

DOI: https://doi.org/https://doi.org/10.5281/zenodo.12805458

Short DOI: https://doi.org/gt442d

Country: India

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Abstract: The deployment of artificial intelligence (AI) systems poses unique challenges compared to traditional software applications, primarily due to the dynamic nature of AI models and their sensitivity to data changes. Continuous deployment (CD) strategies play a crucial role in managing these complexities by enabling organizations to deploy, update, and manage AI models seamlessly and efficiently. This paper reviews key strategies for implementing CD in AI systems, focusing on seamless updates and robust rollback mechanisms. Strategies discussed include incremental deployment, A/B testing, canary releases, and automated rollback procedures, each designed to minimize disruption and optimize performance during model updates. Additionally, the importance of monitoring and feedback loops in ensuring ongoing performance and reliability is highlighted, emphasizing their role in detecting anomalies and integrating user feedback for continuous model improvement. The paper concludes with a discussion on future research directions, including advanced testing methodologies for AI models, scalable deployment strategies across heterogeneous environments, and ethical considerations in AI deployment practices. By addressing these challenges and embracing innovative approaches, organizations can enhance the agility, reliability, and effectiveness of AI deployments, paving the way for broader adoption and impactful application across various domains.

Keywords: Continuous Deployment, AI Systems, Machine Learning Models, Seamless Updates, Rollback Mechanisms

Paper Id: 230793

Published On: 2018-12-05

Published In: Volume 6, Issue 6, November-December 2018

Cite This: Continuous Deployment of AI Systems: Strategies for Seamless Updates and Rollbacks - Swamy Prasadarao Velaga - IJIRMPS Volume 6, Issue 6, November-December 2018. DOI https://doi.org/10.5281/zenodo.12805458

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