There is no doubt of the fact that ML models are some complex entities. These models are trusted by their users with some crucial decision-making tasks. In many cases, the users trust these entities with their lives, such as in the case of autonomous vehicles.
Yet, there are sometimes when those models are forgotten after deploying. The AI and data science teams move on to their next project, and only a critical error within those ML models can be found out only after significant damage takes place.
So, it is important to offer specific considerations regarding ML model performance monitoring to ensure the smooth running of the model.
Proper and correct ML model performance monitoring will help you to detect a specific problem with the model as early as possible and rectify those issues without facing any damage. So, to help you out, here is a comprehensive guide on how you can conduct an accurate ML model performance monitoring.
Why is the procedure of ML model performance monitoring hard?
The procedure of ML model performance monitoring is not a straightforward task, and hence, it cannot be applied easily and correctly for several reasons. One important reason for this is that defining an error is not a simple task, as sometimes ML models can give probabilistic results.
The second reason can be, Machine Learning is a young technology that may work as a bridge between DevOps and Data Science, and hence, it is still in construction. Hence, all these reasons are making it challenging to perform an accurate ML model performance monitoring.
A Comprehensive Guide on How to Monitor Your ML Models in Production
Discussed here are the steps to monitor your ML models in production accurately.
- Granular Monitoring
In order to get a good amount of insights into the performance of your model, it’s crucial to constantly evaluate the model on some particular data or information and even test its performance as well.
If your model is customer-facing, you may need to ensure that your customers can be satisfied by enjoying a good experience. Moreover, you can find out parts with poor performance and get better insights that can help you to improve the performance of your models.
- Monitoring the model
The most proper way to perform your ML model monitoring is to evaluate the performance of your model on real-world data. You can easily create triggers that can help you by informing you when there are any important changes, such as changes in the metrics like precision, accuracy, or even F1.
So, whenever you can find those changes, you can be aware that there is a critical issue with your model or data that need rectification.
- Pattern identifying
In several situations, a decrease in the performance of your model may indicate that may be your model is broken. So, you need to understand if those fluctuations in performances are following a particular pattern. If the answer is yes, then you need to identify that pattern to create a more robust model.
- Finding out issues regarding data integrity
This step is crucial for ensuring the accurate performance of your ML model because this step includes checking the accuracy and consistency of data types, and feature names, identifying new possible values or finding out the missing values, and many more.
- Monitoring the data
Monitoring the data is also essential for finding out if there is an issue with your model and also finding out the main source of the issue.
Therefore, all of these steps are crucial for conducting an accurate ML model performance monitoring and ensuring the smooth performance of your model.
