Radically efficient machine teaching.
An annotation tool powered
by active learning.

From the makers of spaCy
A terminal window
pip install prodigy.tar.gzSuccessfully installed prodigyprodigy dataset tech_orgs "Annotate tech companies"prodigy ner.teach tech_orgs en_core_web_sm "Silicon Valley" --api nyt --label ORG✨ Starting the web server on port 8080...
Open the app in your browser and start annotating!

Train a new AI model in hours

Prodigy is an annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Whether you're working on entity recognition, intent detection or image classification, Prodigy can help you train and evaluate your models faster. Stream in your own examples or real-world data from live APIs, update your model in real-time and chain models together to build more complex systems.

The missing piece in your
data science workflow

Prodigy brings together state-of-the-art insights from machine learning and user experience. With its continuous active learning system, you're only asked to annotate examples the model does not already know the answer to. The web application is powerful, extensible and follows modern UX principles. The secret is very simple: it's designed to help you focus on one decision at a time and keep you clicking — like Tinder for data.

A desktop computer and phone with the Prodigy web application, and a colourful drink with a straw

Try out new ideas quickly

Annotation is usually the part where projects stall. Instead of having an idea and trying it out, you start scheduling meetings, writing specifications and dealing with quality control. With Prodigy, you can have an idea over breakfast and get your first results by lunch. Once the model is trained, you can export it as a versioned Python package, giving you a smooth path from prototype to production.

Buy, keep and customize

As the makers of spaCy, the most popular open-source library for Natural Language Processing in Python, we've seen more and more companies realize they need to invest in building their own AI expertise. AI isn't a commodity you can buy in bulk from a third-party provider. You need to build your own systems, own your tools and control your data. We've built Prodigy with the same philosophy in mind. The tool is self-contained, extensible and yours forever. No matter how complex your pipeline is – if you can call it from a Python function, you can use it in Prodigy.

A laptop with the Prodigy application, a terminal window and a window showing text and dependency parse trees