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Essential guide to Machine Learning Model Monitoring in Production
Techniques to detect data drift
Model Monitoring is an important component of the end-to-end data science model development pipeline. The robustness of the model not only depends upon the training of the feature engineered data but also depends on how well the model is monitored after deployment.
Typically a machine learning model's performance degrades over time, so it's essential to detect the cause of the decrease in performance of the model. The main cause of the same can be drift in the independent or/and dependent features which may violate the model’s assumption and distribution about the data.
In this article, we will discuss various techniques to detect the data drift independent or independent features in the production inference data.
Why Model Monitoring is Required?
There are various reasons why the performance of the model degrades over time:
- Inference Model Performance < Baseline Model Performance
- Inference Data Distribution is different from Baseline Data…