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

From the makers of spaCy
A terminal window
pip install prodigy.whlSuccessfully installed prodigyprodigy dataset reviews "Entities in customer reviews"prodigy ner.teach reviews en_core_web_sm data2017.jsonl --label PRODUCT✨ 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.

If a Bubble Bursts in Palo Alto gpe, Does It Make a Sound?
source: The New York Times
 person  skateboard 
source: Unsplash by: Kirk Morales url: unsplash.com/@knation
Phones

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.

Cloud-free and yours forever

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

Personal

freelancer, indie developer, hobbyist
  • lifetime license with 12 months of free upgrades
  • unlimited use for personal and professional projects
  • Prodigy installer, web application and extensive documentation
$390$290 USD
per individual license

Company

startup, data science team, enterprise
  • lifetime license with 12 months of free upgrades
  • flexible and transferrable floating licenses for you and your team
  • Prodigy installer, web application and extensive documentation
$490$390 USD
per seat, available in packs of 5 seats

Institution

universities and research institutions
  • special site-wide license for degree-granting instutions
  • 12 months unlimited usage for all students and staff
  • Prodigy installer, web application and extensive documentation
$10,000 USD
per year, site-wide
A desktop computer and phone with the Prodigy web application, and a colourful drink with a straw

What you can do with Prodigy

Prodigy's pluggable architecture makes it easy to use your own components for storage, loading, classification, example selection and even annotation. Its built-in capabilities enable easy and powerful workflows:

  • Create, improve or evaluate models for sentiment analysis, intent detection and any other text classification task.
  • Extend spaCy's state-of-the-art named entity recognizer.
  • Improve the accuracy of spaCy's models on exactly the text you're working on.
  • A/B test machine translation, captioning or image manipulation systems.
  • Annotate image segmentation and object detection data.
scikit