The detection of dataset elements that differ significantly from the majority of instances is known as outlier detection. There are various visualization methods and statistical tests, such as z-test, Grubb’s test and other algorithms used to detect them. The Alibi Detect is a toolbox, which is used to detect anomalies such as outliers, dataset drift, and adversarial attacks in a variety of data types such as tabular data, images, time series, and so on, in the context of AutoML. We will discuss this toolbox in detail in this post. Below is a list of the major points to be discussed.