Prodigy Description

Revolutionary machine teaching is here with an exceptionally efficient annotation tool driven by active learning. Prodigy serves as a customizable annotation platform so effective that data scientists can handle the annotation process themselves, paving the way for rapid iteration. The advancements in today's transfer learning technologies allow for the training of high-quality models using minimal examples. By utilizing Prodigy, you can fully leverage contemporary machine learning techniques, embracing a more flexible method for data gathering. This will enable you to accelerate your workflow, gain greater autonomy, and deliver significantly more successful projects. Prodigy merges cutting-edge insights from the realms of machine learning and user experience design. Its ongoing active learning framework ensures that you only need to annotate those examples the model is uncertain about. The web application is not only powerful and extensible but also adheres to the latest user experience standards. The brilliance lies in its straightforward design: it encourages you to concentrate on one decision at a time, keeping you actively engaged – akin to a swipe-right approach for data. Additionally, this streamlined process fosters a more enjoyable and effective annotation experience overall.

Pricing

Pricing Starts At:
$490 one-time fee
Pricing Information:
Per seat, available in packs of 5 seats

Integrations

API:
Yes, Prodigy has an API

Reviews

Total
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features
design
support

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Company Details

Company:
Explosion
Year Founded:
2016
Headquarters:
Germany
Website:
prodi.gy/

Media

Prodigy Screenshot 1
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Product Details

Platforms
Windows
Mac
Linux
Types of Training
Training Docs
Customer Support
Online Support

Prodigy Features and Options

Data Labeling Software

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Machine Learning Software

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Natural Language Processing Software

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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