Prodigy is a modern annotation tool for creating training and evaluation data for machine learning models. You can also use Prodigy to help you inspect and clean your data, do error analysis and develop rule-based systems to use in combination with your statistical models.

The Python library includes a range of pre-built workflows and command-line commands for various tasks, and well-documented components for implementing your own workflow scripts. Your scripts can specify how the data is loaded and saved, change which questions are asked in the annotation interface, and can even define custom HTML and JavaScript to change the behavior of the front-end. The web application is optimized for fast, intuitive and efficient annotation.

Prodigy’s mission is to help you do more of all those manual or semi-automatic processes that we all know we don’t do enough of. To most data scientists, the advice to spend more time looking at your data is sort of like the advice to floss or get more sleep: it’s easy to recognize that it’s sound advice, but not always easy to put into practice. Prodigy helps by giving you a practical, flexible tool that fits easily into your workflow. With concrete steps to follow instead of a vague goal, annotation and data inspection will change from something you should do, to something you will do.