There is no catch-all method to ensure that model drift is detected and addressed in a timely manner. Whether it is through scheduled model retraining or through real-time machine learning; creating a sustainable machine learning model is a challenge in and of itself.
However, the advent of MLOps has simplified the process of retraining models more often and within shorter intervals. It has enabled data teams to automate the model retraining and the most surefire approach to trigger the process is by scheduling. With automated retraining, companies can fortify the existing data pipeline with new and fresh data within a specific time frame. The good thing is that it doesn’t require any specific code changes or rebuilding of the pipeline. If a company, however, discovers a new feature or algorithm which was not previously available during model training, then including it while deploying the retrained model can significantly enhance model accuracy.
When deciding the frequency with which models need to be retrained, there are several variables to consider. Sometimes waiting for the problem to show itself becomes the only real option (particularly if there is no past history to work with going forward). In other situations, models should be retrained based on patterns tied to seasonal changes in variables. What remains constant in this sea of change, however, is the importance of monitoring. Regardless of schedules or the business domains; constant monitoring at regular intervals is and always will be the best way to detect model drift.
While the challenges of managing, detecting, and addressing model drift across thousands of machine learning models may seem daunting, Machine Learning Operationalization Solutions from service providers such as Sigmoid can give you the edge you need to face these issues head-on. Sigmoid’s MLOps practice provides the right mix of data science, data engineering, and DataOps expertise, required to operationalize and scale machine learning to deliver business value, and build an effective AI strategy.