Imagine you have a machine learning model in production, and some features are very volatile. Their distributions are not stable. What should you do with those? Should you just throw them away?
Data and prediction drift often need contextual interpretation. In this blog, we walk you through possible scenarios for when you detect these types of drift together or independently.
Even if you can calculate the model quality metric, monitoring data and prediction drift can be often useful. Let’s consider a few examples when it makes sense to track the distributions of the model inputs and outputs.
What can you do once you detect data drift for a production ML model? Here is an introductory overview of the possible steps.
When monitoring ML models in production, we can apply different techniques. Data drift and outlier detection are among those. What is the difference? Here is a visual explanation.
Can you train a machine learning model to predict your model’s mistakes? Nothing stops you from trying. But chances are, you are better off without it.
