Understanding Model Monitoring
Monitoring machine learning models entails closely observing and gauging the performance of these models during the development and operational phases. When a model transitions from an experimental setup to a production scenario, the outcome can be influenced by the change in the environment. It's crucial to remember that the main objective of a model is to fulfill a business need and enhance a company's value. As such, it has to meet certain key business prerequisites: it should maintain consistency and relevance throughout the development lifecycle.
Why Model Monitoring Matters?
Proper configuration of Machine Learning Model Monitoring is essential to promptly detect any pitfalls that might occur with the deployed models. As a part of the MLOps framework, machine learning implementations need to be monitored to maintain the health of the model and to intervene when the performance metrics show a decline. Potential issues include Data Skews, Data Dependencies, and Model Staleness.
Understanding The Various Issues That Can Occur
In the case of data skews, the discrepancy arises when the data used in the training phase of the model does not accurately represent the live data. This could occur due to several reasons like incorrect design of the training data, absence of a feature in production, or a mismatch between the research and live data.
Model staleness is another common issue that can occur due to rapid changes in the environment, evolving tactics by bad actors seeking to exploit the model, and shifts in customer preferences impacted by multiple factors. These risks must be diligently monitored, especially in recommendation ML systems.
Moreover, Negative Feedback Loops can be caused if automatic model training is applied to data procured during development. If this data is skewed or biased, the resulting models will underperform.
Methods To Gauge Model Performance
The performance or accuracy of the model(s) during development is of vital importance. However, it's often not feasible to directly gauge performance, such as in models designed to detect fraud. The only way to determine accuracy in such scenarios is through criminal investigations or other tests. Similar situations are encountered in areas such as disease risk prediction, future property value estimation, credit risk prediction, and long-term stock market predictions. Therefore, monitoring proxy values in production is a sensible way to ensure model accuracy.
Best Practices For Model Monitoring
- Model Performance Monitoring: This essentially involves verification that input values are within a permissible range. Watching for alterations in null features can indicate a data bias or changes in customer behavior, warranting deeper investigation.
- Monitoring Model Versions: This often-overlooked aspect involves keeping track of the deployed version of your model, since configuration errors can occur.
In conclusion, the importance of model monitoring in machine learning cannot be neglected. It's an essential tool for identifying potential issues, ensuring accuracy, and maintaining the ongoing health of your machine learning models.