Data drift can lead to inaccurate predictions and poor machine learning model performance. TorchDrift is a PyTorch-based library that offers several ways to detect data drift, including kernel Maximum Mean Discrepancy (MMD). TorchDrift can be used on tabular data, like the Penguins dataset from seaborn library, and time series data. The article covers how to convert numpy data into torch tensors, manipulate data, and plot output to estimate statistical significance between distributions. TorchDrift has five different detectors, but the tutorial focuses on kernel MMD drift detection.

source update: Drift Detection Using TorchDrift for Tabular and Time-series Data – Towards AI


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