Anyone who works with machine learning models likely has an idea of how essential it is to have an effective ML model monitoring framework in place. This helps to ensure the model’s accuracy — and, in turn, its reliability — in making correct predictions.
But what makes ML model monitoring so important, and how do you build a proper model monitoring framework to track and evaluate an ML model in production?
Model monitoring is essential throughout a model’s lifecycle to ensure it maintains accuracy over time. As the model processes new data, a certain amount of model degradation is inevitable. One of the biggest reasons behind this is the phenomenon of concept drift, described by one Cornell University study as being caused by “unforeseeable changes in the underlying distribution of streaming data over time.”
Model monitoring helps keep deployed models on track, offering the ability to monitor for things like model drift, negative feedback loops, and other indicators of an inaccurate-leaning model. With an ML model monitoring framework in place, it becomes much easier for Machine Learning Operations (MLOps) teams to:
Effectively monitoring ML models in production entails two crucial elements: clear, actionable ML model monitoring metrics and high-quality machine learning model monitoring tools.
There are a few different ML model monitoring metrics, each of which will help you understand how well the model is performing — and detect/triage issues before they become too problematic.
These metric types include:
You can learn more about the metrics you should be paying attention to — and how to calculate them — in our Ultimate Guide to ML Model Performance.
A wide variety of ML monitoring solutions are available, meaning organizations can seek out tools that are intuitive and aligned with the distinct needs of their MLOps lifecycle. When evaluating ML monitoring tools, some of the most important features to consider:
An ML model monitoring framework provides a reliable system for monitoring and managing models both in training and production. It includes bringing the right tools, personnel, and model monitoring best practices together to provide a comprehensive and actionable view of model performance. Implementing a well-designed ML model monitoring framework helps organizations consistently:
The best tools to support a monitoring framework will offer both model monitoring and explainable AI capabilities within a single model performance management (MPM) platform An MPM platform offers compelling benefits:
Request a demo to see how Fiddler can help you monitor and explain your models.