  We present Azimuth, an open-source and easy-to-use tool to perform error
analysis for text classification. Compared to other stages of the ML
development cycle, such as model training and hyper-parameter tuning, the
process and tooling for the error analysis stage are less mature. However, this
stage is critical for the development of reliable and trustworthy AI systems.
To make error analysis more systematic, we propose an approach comprising
dataset analysis and model quality assessment, which Azimuth facilitates. We
aim to help AI practitioners discover and address areas where the model does
not generalize by leveraging and integrating a range of ML techniques, such as
saliency maps, similarity, uncertainty, and behavioral analyses, all in one
tool. Our code and documentation are available at
github.com/servicenow/azimuth.
