aws-samples/amazon-sagemaker-drift-detection
This sample demonstrates how to setup an Amazon SageMaker MLOps end-to-end pipeline for Drift detection
This project helps MLOps engineers set up a robust, automated pipeline for machine learning models. It takes your model code and data, then automatically builds, trains, deploys, and monitors your models for performance degradation over time. The output is a continuously monitored, production-ready machine learning model that alerts you to potential issues like data drift or concept drift.
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Use this if you need to reliably deploy and maintain machine learning models in production, automatically detecting when their performance degrades due to changes in data or behavior.
Not ideal if you're looking for a simple, one-off model deployment or if you don't require continuous monitoring for model drift.
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Python
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MIT-0
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Oct 17, 2023
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