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Model drift refers to the change in the statistical properties of the target function that a machine learning model is trying to approximate. This can happen over time as the distribution of the data changes, resulting in a mismatch between the training data and the test data.
For example, an eCommerce platform might use a machine learning model to detect fraud and identify fake stores or products. As the platform grows and incorporates more users, stores, and products, the model must adapt to the new data. Otherwise, the predictions become less accurate and the model will fail to identify fraudulent sellers.
Organizations can experience the negative effects of model drift gradually or suddenly. To prevent model drift, it is important to monitor the performance of machine learning models to identify their decay and assess the causes of the drift.
This is part of a series of articles about data drift.
Model drift is important because it can significantly impact the performance of a machine learning model on a production dataset. If a model experiences drift, it can lead to the model making incorrect or suboptimal predictions or decisions, which can have serious consequences for an organization.
For example, if a model that is used to make credit decisions begins to drift, it may start to approve loans for risky borrowers who are likely to default, leading to financial losses for the lender. Similarly, if a model that is used to predict equipment failures in a manufacturing plant begins to drift, it may start to miss important signals, leading to unexpected downtime and reduced productivity.
In addition to the immediate consequences of model drift, it can also lead to a loss of trust in the AI system and a decline in its adoption.
There are several causes of model drift, which can be broadly categorized as:
Accurately detecting model drift is important to ensure that machine learning models continue to perform well over time. There are several techniques that can be used to detect model drift, including:
There are several best practices that can be used to avoid model drift and ensure that machine learning models continue to perform well over time:
To avoid model drift, regularly monitor and evaluate the performance of machine learning models, establish a data quality assurance process, retrain the model on updated data, implement feedback loops and user testing, and continuously evaluate the model’s performance against business metrics using monitoring tools.
Aporia’s ML observability platform is the ideal partner for Data Scientists and ML engineers to visualize, monitor, explain, and improve ML models in production. Our platform fits naturally into your existing ML stack and seamlessly integrates with your existing ML infrastructure in minutes. We empower organizations with key features and tools to ensure high model performance:
To get a hands-on feel for Aporia’s advanced model monitoring and deep visualization tools, we recommend:
Book a demo to get a guided tour of Aporia’s capabilities, see ML observability in action, and understand how we can help you achieve your ML goals.