The pipeline used to generate complete data science models includes model monitoring as a key step. The model’s resilience is influenced by the development of the features engineered data and the quality of the model’s post-deployment monitoring.
It’s common for an ML model performance monitoring to deteriorate over time; thus, it’s critical to identify what is contributing to the model’s performance decline. The major reason might be wandering in the dependent or/and independent characteristics, which could go against the model’s distributional assumptions.
This post discusses several methods for finding features in production inference data that are independent of data drift.
Why is Model Monitoring Necessary?
The model’s performance deteriorates with time for a number of reasons:
- Performance of the baseline model minus that of the inference model
- The distribution of inference data differs from that of baseline data
Adaptation of Business KPI
The main causes of a model’s performance deterioration over time are those already discussed. After deployment, the deployed model has to be monitored to assess model effectiveness and data dispersion. The pre-existing model is re-trained using the new dataset after the reason for the model deterioration has been identified.
Measure the Independent Features’ drift
There are many possible ways to keep track of the independent features’ drift.
Follow the Statistical Features
To track the dispersion in the dataset, one must follow the statistical characteristics of the inferences and baseline data to track the dispersion in the dataset. Among the statistical characteristics are:
Possible value range
- Number of NULL or missing values
- Distribution of numerical characteristics in a histogram
- Distinct Categorical Feature Values
Observe how each characteristic is distributed:
We can anticipate deterioration in model performance if we notice any changes in the dispersion of the inference data’s engineered or raw characteristics.
Watch the multivariate feature distribution:
In order to produce predictions, machine learning models create some connections between the features. The performance of the model may suffer if the structure or distribution of the characteristics is altered.
How is ML model performance monitoring carried out?
The actual target classifier for the reasoning data is typically not available beforehand. Therefore, it is challenging to assess the model’s performance using common assessment measures like accuracy, recall, precision, log-loss, etc. The appropriate class label may not always be accessible right away.
But looking at the data distribution is another way to assess the model’s robustness. The data drift in independent and dependent characteristics may be measured using a number of different methods.
Measure the dependent feature drift:
During production, the reliant feature (goal label) again for inference target class could not be available.
And once the reliant feature is established, several methods for measuring drift and determining whether or not the model’s performance has worsened.
Distribution of the Target Class:
The goal class label for the classification job is discrete in nature. The purpose is to contrast how the target class labels are distributed across the base and inference data.
Inference model performance should be monitored
Once the real target classifier is made accessible, model drift may be found by comparing and analyzing the model’s performance on common metrics.
Several methods for detecting drift in the inferences dataset after model deployment in production have been covered in this article. Model drift might cause the situation to get worse.
Over time, ML model performance monitoring may decline as a result of model drift. Therefore, it’s important to monitor the model’s performance once it has been implemented.
