Model monitoring is the tracking of the ML models’ performances so that the AI teams and ML models can find out the issues before using those models in the businesses.
A strong ML monitoring infrastructure can proactively assess the relevance of data, monitor the health of service and the performance of the model, and also implements trust elements like bias and fairness. So, problem-free ML monitoring is always crucial for the accurate performance of those machine learning models.
Why is ML monitoring crucial?
There are many reasons why you should monitor your models properly. It allows you to analyze the authenticity and accuracy of the Prediction, boost the models to ensure their better performance, and also to eliminate prediction errors.
- ML monitoring ensures the stability and accuracy of Prediction
If changes happen in any part of the system, there can occur some unpredictable changes. ML monitoring makes sure that you can get a stable prediction just by tracking several metrics regarding stability, including CSI or Characteristic Stability Index and PSI or Population Stability Index.
- ML monitoring eliminates poor generalization
Sometimes due to the lack of proper data or other computational elements, a machine learning model needs to be trained on just a small subset. Thus, the sample of output data becomes inefficient or inaccurate. This issue can be fixed by model monitoring.
Five components of model monitoring
There are some specific components that you need to monitor to complete the procedure of model monitoring. Five of those components are:
- Performance shifts
With the help of performance shifts, you can easily determine how your model is performing. Moreover, this process helps you to know how to effectively debug if you encounter something wrong.
The easiest way to track performance shifts is to continuously evaluate the performance on real data. To determine whether the model is performing accurately or not, you can compare those predictions to the real values. You can also trigger customized notifications for collecting information regarding any changes in precision and accuracy.
- Data distribution shifts
All ML models degrade over time. So, you might face data drift while making some changes in the input of the model. If the model can receive data that was not apparent in the training process, the performance can deteriorate a lot.
Hence, it is crucial to get early signals regarding the changes in data distribution. Thus, you can easily update your models according to that data. With the help of monitoring models, one can easily track the distributions for the model prediction and also for the key features of the models.
- Data integrity
Data integrity is related to the reliability of data. You need to ensure that the data is correct. There are a few ways through which you can do it, including validation and error checking.
In the process of data distribution, you need to check errors, correlations, and feature statistics to ensure integrity. Through data integrity, you can test documents and profiles of your data.
- Operation Metrics
While conducting ML monitoring, you need to give proper attention to the performance of the system, including the usage of memory, CPU, disk, and also network I/O. properly analyzing those metrics is very crucial for making sure your machine learning model is operating properly.
- Fairness
Monitoring fairness or bias makes sure that all parts of the organization can have fair treatment. It is crucial in the sectors like finance, and healthcare, where decisions regarding models might have some serious issues.
Therefore, all these five components can help you in accurate and successful ML monitoring.
