Machine learning (ML) models are increasingly being used for a variety of use cases across all industries. But all models will inevitably experience degradation and data drift over time, leading to underperformance. Model monitoring aims to alleviate that issue before it results in adverse impacts.
AI model monitoring alerts users when an issue in a model starts so that you can fix and retrain it if necessary. The model monitoring framework runs from the development phase all the way through the deployment, operation, and retraining phases so that any problems can be fixed quickly and your model can run as productively as possible.
Machine learning model monitoring in production as well as before deployment is needed due to model degradation. To continue to work effectively, your models must be monitored for any performance decline. Such issues may be caused by factors such as data drift and model bias.
Data drift (as well as model drift, feature drift, and concept drift) occurs when there is a significant change from training data to data ingested in production. These shifts happen because the assumptions used in the training stage no longer apply during production. They may be caused either by:
Model monitoring is necessary to stop data drift and model bias from occurring or to catch errors that the model makes so that you can retrain it for better, more accurate performance.
There are a few ways to monitor a machine learning model. For one, you can compare model outputs to ground truth. Ground truth refers to baseline data that can be used as the standard for the data your model outputs. For this method of monitoring, you merely have to calculate the accuracy of production data based on the standard you set. However, baseline data isn’t always available in the real world or may take too long to gather, so other methods of monitoring become necessary.
Another such method is to examine target variables and input features. That’s because a model degrades due to a changing relationship between input features and their target variables. To determine the accuracy of your model using model monitoring metrics, find the difference between your initial data set and a second one.
Models can also monitor by analyzing feature importance. Look at shifts in the importance of certain features as well as changes in the ways those features are ordered in importance.
It is important to note that on top of monitoring your output data, you should monitor your input data, too. The assumptions initially made when training your model may no longer apply based on what’s happening in the real world.
Model monitoring of machine learning models is important for several reasons:
To learn more about model monitoring and how it works, try Fiddler today.