Experimenting, building a model, and putting it into production takes a long time. The time difference might range from months to years. The distribution of data may vary throughout this time gap, resulting in differences between the data used to train and create the model and the data that the model encounters in the production environment.

The performance of models degrades over time as a result of this drift, resulting in weak and declining predictive performance in predictive models. This is a typical occurrence, but it is a significant problem in performance-critical Machine Learning systems.

Today’s data is changing and evolving at a breakneck speed. It’s critical to keep up with shifting data if you require high-performance models. As a result, it’s critical to spot the point in production where your data diverges from the one it was trained on, ensuring that they remain relevant in production and provide fair and unbiased predictions over time; otherwise, if these drifts go undetected, predictions will be incorrect, and business decisions may have a negative impact.

Model drift may be caused by a variety of variables, the most common of which are data drift, prior probability drift and the concept drift. Because these drifts entail a statistical change in the data, a variety of statistical and model-based features, such as Kullback-Leibler divergence, Kolmogorov-Smirnov test, and others, might be used to detect them.

Through the session, better understand the topic of model drifts and how they may be monitored and evaluated in real time using a repeatable method in order to minimize future mishappenings.