The following tables summarize the advised use cases for the current algorithms. Please consult the method specific pages for a more detailed breakdown of each method. The column Feature Level indicates whether the detection can be done and returned at the feature level, e.g. per pixel for an image.
Outlier Detection Adversarial Detection Drift DetectionAll drift detectors and built-in preprocessing methods support both PyTorch and TensorFlow backends. The preprocessing steps include randomly initialized encoders, pretrained text embeddings to detect drift on using the transformers library and extraction of hidden layers from machine learning models. The preprocessing steps allow to detect different types of drift such as covariate and predicted distribution shift.
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