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  • 4 min read

Don’t Drift Away from Reality — The Importance of AI Model Longevity

  • July 30th, 2024

Author's

George Harrison

George Harrison

Artificial Intelligence Consultant

George is an Oxford alumni mathematician and AI software developer. He has a deep rooted understanding of how AI works and how to apply it in real-world working environments. He is guest writing for Actionary on deep AI topics.
Simon Harrison

Simon Harrison

Analyst and Client Executive Partner

Simon is an industry analyst who has authored over 30 Gartner Magic Quadrant notes as lead and with colleagues. He’s written important research as the Chief of Research advisor for Gartner. He continues to provide deep research and insights as an Executive Partner for Actionary clients and the industry.

Summary

In the ever-evolving landscape of machine learning (ML), winning the battle against model drift and decay is pivotal to maintaining a competitive edge and achieving sustainable success. Model drift and decay refer to the degradation in the performance of machine learning models that happens over time. This research delves into the critical importance of addressing these issues head-on and offers actionable insights to support verifying investment in the right Artificial Intelligence (AI) vendor solutions. Continuous monitoring, implementing automated retraining pipelines, and ensuring robust data management safeguard the integrity of AI systems and unlocks their full potential. Discover how proactive measures can ensure long-term value for your organization and what to be asking AI solution providers.

Key Take: Effective management of model drift and decay is essential for sustaining the value of ML systems. Without these measures, models can become obsolete, leading to inaccurate predictions and reduced ROI.

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