There are many types of drift, so how do you troubleshoot model drift before it impacts your business’s bottom line?
Drift monitoring
Stay on top of data, concept, and model drift, and know when to optimize and retain your models.
Feature stability
I want to monitor distribution shift for top 5 important features in all fraud models across entire set
Makes sure that your models are reliable and that your top features contribution to ML predictions remains consistent.
Training-serving skew
I want to monitor training-serving skew in 4 models across entire set
Detect distribution discrepancies between the data used to train your machine learning model and the data encountered in production.
Dataset shift
I want to monitor input drift for all entities in all fraud models across all segments comparing last week to same period last month
Monitor high dimensionality scenarios with dataset shift monitoring to quantify drift for the entire dataset together and drill down to understand precisely what is drifting and where it’s drifting.
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Featured resources
A hands-on introduction to drift metrics
Instead of focusing on theoretical concepts, this post will explore drift through a hands-on experiment of drift calculations and visualizations. The experiment will help you grasp how the different drift metrics quantify and understand the basic properties of these measures.
Concept drift detection basics
This article will illustrate how you can use Layer and Amazon SageMaker to deploy a machine learning model and track it using Superwise.
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