Machine Learning Models: Deployment Activities

Creating a machine learning model and deploying it in production takes effort. We had previously discussed the various ways in which we can deploy models in production. However, model deployment is not the end; it is just the beginning. The real issues start from here. We don't have any control over the data in the actual environment. Changes might happen, and we need to be ready to detect and upgrade our model before it becomes obsolete. In this piece, we will discuss some ways to monitor model performance on an ongoing basis. A machine learning model is built on a set of input training data with various attributes. So, the most important facet is to check to see if the input data on which the model was trained still holds good on the actual data in the real world environment. This terminology is primarily known as Concept Drift. The change in data might be sudden, or it might change gradually over time. So, it is essential to identify the change patterns and fix the model beforehand.

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This article is related to

machine learning,artificial intelligence,data science,concept drift,model deployment