Mitigating drift

The world of ML is ever-evolving, making it crucial for us to remain adaptable. We’ve seen how the concept of drift is integral to understanding changes in our data or model over time. But what can we do when faced with these shifting sands? Are we merely left to witness the disintegration of our model’s performance? Not quite. This section presents actionable strategies for mitigating drift, each one holding its unique place in our toolbox for effective drift management.

Understanding the context

Before we delve into the technicalities of mitigating drift, let’s acknowledge the necessity of understanding the context in which our model operates. Just as a ship captain needs to understand the sea and the weather conditions, we need to comprehend our data sources, user behavior, environmental changes, and all other nuances that form the backdrop against which our model functions.

Consider an e-commerce recommendation system. Understanding the context would mean being aware of seasonal trends, ongoing sales, or any recent global events that could influence customer behavior. For instance, during a global sporting event, there might be a surge in sports-related purchases. Being aware of these contextual cues can help us preempt drift and prepare our models to adapt.

Continuous monitoring

Knowledge without action is futile. Once we’re familiar with our context, the next step is to keep a vigilant eye on our model’s performance. We need to continuously monitor the heartbeat of our models and data. This could be achieved by tracking model performance metrics over time or using statistical tests to identify significant shifts in data distributions.

Take the case of a credit scoring model. If we notice a sudden surge in the number of credit defaults, it might indicate a drift that needs our attention. Monitoring systems such as dashboards with real-time updates can prove to be valuable assets in catching these shifts before they snowball into more significant problems.

Regular model retraining

Stagnation is the enemy of progress. As the world around us changes, our models need to keep up by learning from fresh data. Regularly retraining the model can help it stay updated with recent trends and patterns. How often should we retrain? Well, it depends on the velocity of change in our data or context. In some cases, retraining may be necessary every few months, while in others, it might be required every few days.

Consider a model predicting stock market trends. Given the volatility of the markets, the model might benefit from daily or even hourly retraining. Conversely, a model predicting housing prices might only need semi-annual retraining due to the relative stability of the housing market.