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...