Engineering Systems for Dynamic Retraining and Deployment of AI Models

Authors: Balaji Soundararajan

DOI: https://doi.org/10.5281/zenodo.15054625

Short DOI: https://doi.org/g88378

Country: USA

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Abstract: The increasing reliance on artificial intelligence (AI) in dynamic business environments enables the adaptive model management systems to mitigate performance degradation caused by evolving data patterns, operational shifts, and market changes. Traditional retraining methods are resource-intensive and struggle to maintain consistency, prompting the need for innovative approaches such as Just-in-Time (JIT) retraining, real-time monitoring, and automated deployment pipelines. We will examine the engineering challenges of designing adaptive AI systems, including scalability, computational costs, privacy concerns, and integration complexities. We will learn the role of continuous learning frameworks, transfer learning, and ensemble techniques in enabling efficient model recalibration. Case studies from urban automation, conversational AI, and industrial applications illustrate practical implementations of dynamic retraining systems, emphasizing reduced technical debt and improved operational resilience. We will also extend our focus on best practices for monitoring model drift, deploying CI/CD pipelines, and balancing human oversight with automation. By synthesizing research and real-world applications, this work provides a roadmap for organizations to enhance AI reliability, adaptability, and trustworthiness in production environments while addressing ethical and compliance risks.

Keywords: Adaptive AI systems, model drift, Just-in-Time (JIT) retraining, continuous integration/continuous deployment (CI/CD), machine learning operations (MLOps), real-time monitoring, transfer learning, concept drift, automated model deployment, industrial AI applications

Paper Id: 232261

Published On: 2023-04-05

Published In: Volume 11, Issue 2, March-April 2023

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