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The problem of ML Model drift and decay in production
Manage episode 456086283 series 3620285
Вміст надано David Such. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією David Such або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
In this episode, called “The Problem of ML Model Drift and Decay in Production,” we explore the challenges of maintaining machine learning (ML) model accuracy over time. We break down model drift, a critical issue where a model’s predictive performance degrades due to changes in data or the environment. Listeners will learn about the two main causes of drift: data drift, where input data distributions shift, and concept drift, where the relationship between inputs and outputs evolves.
We also discuss the real-world consequences of model drift, such as poor decision-making, business losses, and ethical concerns like biased predictions. To address these challenges, we outline best practices for mitigating drift, including continuous monitoring, maintaining data quality, implementing regular retraining cycles, and leveraging specialized tools and technologies. Finally, we highlight the broader business and ethical implications of neglecting model drift, emphasizing why proactive strategies are essential for ensuring long-term ML model reliability.
If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!
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