Best practice: Continuous model validation

This article provides a comprehensive guide to continuous model validation, emphasizing its critical role in maintaining the accuracy and effectiveness of your deployed Snorkel Flow models. Continuous model validation refers to the regular assessment of a model to ensure it continues to perform as expected despite changes in the data it processes or in its operational environment. This article details methodologies for regularly assessing model performance, which equips you to identify and address deviations caused by changes in the underlying data, also known as data drift. Continuous model validation ensures the long-term reliability of machine learning models used in production. Snorkel recommends you be conduct model validation periodically or when you suspect performance degradation. Adhering to these practices sustains operational success because the validation helps mitigate the risk of performance decline in response to changing data landscapes and shifting business needs.

Importance of model validation

Continuous model validation is critical in the machine learning lifecycle. This best practice ensures that models maintain high performance despite changes in incoming data. Regular checks help identify deviations from expected performance metrics like Accuracy, F1 Score, and Precision, which can degrade due to shifts in the underlying data. For example, a model predicting seasonal temperatures might require adjustments as conditions change throughout the year. Consistent evaluation is indispensable for long-term reliability and provides insights into setting the right validation frequency based on your model’s impact and the data it processes.

Establishing a model validation cadence

It is crucial to choose an appropriate validation frequency. The cadence should reflect the importance of the model’s output and the dynamic nature of the data it handles. These are the key considerations for developing your model validation schedule:

1. Severity of Impact: Models critical to business operations or with high stakes in output accuracy should undergo more frequent validations.
2. Nature of Data: Models using data that frequently updates or significantly changes should be validated more often to quickly adapt to new conditions.
3. Volume of Data: High-volume data applications are more susceptible to variations in model performance, necessitating shorter intervals between validations.

Snorkel AI recommends collaborating with your Snorkel Machine Learning Success Manager to tailor a validation strategy that best supports your needs.

Steps for Effective Model Validation

To ensure comprehensive validation, follow these steps as they fit your specific application requirements:

1. Assess the latest baseline metrics. Start by reviewing the current model’s latest baseline metrics. These metrics are helpful to compare the performance of the model on new production data.
2. Identify representative data samples. Gather a diverse dataset from production that covers various times, locations, and all relevant labels. Aim for a random yet representative sample.
3. Label the validation dataset. If not already labeled, manually label the new dataset in Snorkel Flow for direct validation use.
4. Evaluate model performance. Analyze how the current model performs with the new data. If the results fall below acceptable thresholds, consider revising the labeling functions and model configurations.
5. Refine and retrain your model. Use these insights to update your labeling functions and retrain the model using the enriched data set within Snorkel Flow.

Note: While Snorkel Flow facilitates steps 1, 3, 4, and 5, you will need to conduct step 2 externally.