MLOps
Estimating Performance of Regression Models Without Ground-Truth
Sounds like magic, but it’s actually quite simple.
Deploying a machine learning model to production is just the first step in the model’s lifecycle. After the go-live, we need to continuously monitor the model’s performance to make sure the quality of its predictions stays high. This is relatively simple if we know the ground-truth labels of the incoming data. Without them, the task becomes much more challenging. In one of my previous articles, I’ve shown how to monitor classification models in the absence of ground truth. This time, we’ll see how to do it for regression tasks.
Performance monitoring, again
You might have heard me use this metaphor before, but let me repeat it once again since I find it quite illustrative. Just like in financial investment, the past performance of a machine learning system is no guarantee of future results. The quality of machine learning models in production tends to deteriorate over time, mainly because of data drift. That’s why it is essential to have a system in place for monitoring the performance of live models.
