Machine learning (ML) model monitoring is essential to ensure your models function properly. Without consistent monitoring, your models will degrade and become less accurate over time. How can you avoid significant degradation? model monitoring best practices suggest you frequently check the precision of your model to identify when problems occur before they have negative impacts. Let’s explore ML model monitoring and degradation.
ML monitoring is used to gauge critical model performance indicators and identify when problems occur. Monitoring involves observing any changes in your ML models, such as model degradation or data drift, and making sure that your model is still performing as intended. Model monitoring helps your MLOps team find and fix a wide range of problems, such as inaccurate predictions and subpar technical performance.
Basically, the purpose of continuously checking your models is to:
Lack of monitoring throughout the model’s life cycle comes with many risks. Since you can't explicitly test for every scenario a model may encounter, you must constantly check on it to make sure it's working properly. Many ML models are put into use without sufficient testing or monitoring, despite the possibility of negative impacts. Additionally, once a model is deployed, its predictive performance tends to deteriorate almost immediately.
Depending on the variables and parameters at the time the model was created, it can be optimized to perform based on the most recent data it is given. Since ML makes predictions for a dynamic environment, ML models must continually be adjusted in order to maintain performance. As you modify a model, the data that you used to train your model loses relevance over time. Models developed with outdated information may not only be inaccurate but also irrelevant, rendering the predictions worthless or even destructive. Without dedicated model monitoring as part of the MLOps lifecycle, ML teams cannot know or accurately recognize when this occurs.
Simply put, once your model is deployed, it’s at risk of inaccurately predicting results compared to training. It is misleading to assume the deployment of a trained model means the end of ML development. Machine learning models are frequently created to consume future unknown data. As a model is tested on current datasets in quickly changing contexts, the model’s predictive ability inevitably declines. This change in accuracy leads to model degradation of machine learning solutions.
The process of latent performance decreasing is known as model drift. Model drift describes how the relationship between input and output data changes over time in unexpected ways. Because of the changes, the end-user interprets the model predictions for the same or comparable data as having degraded. Model drift essentially refers to a shift in the underlying and overlooked connection between input and output variables.
As data is collected, pay attention to how your model performs after deployment and compare any changes to how well it functioned during training. If you see a decline in model performance, like model drift, then it's time to retrain the model. Modern machine learning model training takes time, and updating a model that has already been trained and tested may be challenging and take significant time and resources.
You'll feel more at ease knowing your model is performing as intended if you continuously evaluate ML model performance indicators. The best way to check the performance of your model is to use ML model monitoring tools. For example, Fiddler offers enterprise-grade model monitoring as part of its AI observability platform, giving you ongoing visibility into your training and production machine learning, enabling teams to react to actionable insights to enhance models, and helping you understand why predictions are made. Try Fiddler for free today!