Drift in Machine Learning

Introduction

Let's delve into machine learning drift, including target drift, data drift, and concept drift, to gain a deeper understanding of how these variations can impact model performance. Following that, we can explore potential solutions to implement drift detection in a production-grade project.

Welcome to Slidev!

To start the slide show:

• npm install
• npm run dev
• visit http://localhost:3030

Edit the slides.md to see the changes.

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References

• https://towardsdatascience.com/drift-in-machine-learning-e49df46803a
• https://madewithml.com/courses/mlops/monitoring