Exploring strategies to handle drift

Simply setting up drift monitoring for a deployed model isn’t enough to effectively tackle all potential drift-related challenges. It’s crucial to ask yourself: does the specific drift with the chosen data type impact the model’s performance in the metrics that matter the most? At what point does drift become intolerable? To properly address drift, start by pinpointing the drift metric and data type that carries the most significance for your model and the business. If your model has been developed correctly, it may possess generalizable properties, which is the primary goal for most machine learning practitioners. This means that a well-developed model should be able to handle drift effectively. When drift detection and alerts are configured without proper consideration of their effects, it poses the risk that drift alerts can be raised without an actual issue, which can result in wasted time and resources that could have been...