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David Such에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 David Such 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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The problem of ML Model drift and decay in production

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Manage episode 456086283 series 3620285
David Such에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 David Such 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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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!

  continue reading

20 에피소드

Artwork
icon공유
 
Manage episode 456086283 series 3620285
David Such에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 David Such 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Send us a text

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!

  continue reading

20 에피소드

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