Prodigy AI is a scriptable annotation tool designed for rapid data labeling in NLP and computer vision tasks. It leverages active learning to make the process efficient.
Prodigy AI is a scriptable annotation tool designed for rapid data labeling in NLP and computer vision tasks. It leverages active learning to make the process efficient.
Prodigy AI is a highly efficient, scriptable annotation tool that utilizes active learning to accelerate the creation of training datasets for machine learning models. It supports tasks in natural language processing (NLP) and computer vision such as text classification, named entity recognition, object detection, and image segmentation. With an extensible back-end, Prodigy enables users to rapidly iterate and refine their models, reducing the time and cost usually required for data annotation.
Who will use ProdigyAI?
Data Scientists
Machine Learning Engineers
NLP Researchers
Computer Vision Specialists
Software Developers
How to use the ProdigyAI?
Step1: Download and install Prodigy AI.
Step2: Set up your project configuration.
Step3: Choose a built-in recipe or create a custom one.
Step4: Start the annotation process and leverage active learning to make decisions.
Step5: Review and export your annotated data for model training.
Platform
web
mac
windows
linux
ProdigyAI's Core Features & Benefits
The Core Features of ProdigyAI
Scriptable annotation tools
Active learning
Extensible back-end
Support for NLP and computer vision tasks
The Benefits of ProdigyAI
Rapid data annotation
Improved model accuracy
Reduced cost and time for data preparation
Customizable workflows
ProdigyAI's Main Use Cases & Applications
Text classification
Named entity recognition
Object detection
Image segmentation
Custom ML tasks
FAQs of ProdigyAI
Is Prodigy AI free?
No, Prodigy AI requires a paid license to use.
What platforms does Prodigy AI support?
Prodigy AI supports web, Windows, macOS, and Linux platforms.
Can Prodigy be used for computer vision tasks?
Yes, Prodigy supports tasks like object detection and image segmentation.
What is active learning in Prodigy?
Active learning helps the model to decide which samples need to be annotated, making the process more efficient.
Can Prodigy export annotated data?
Yes, the annotated data can be exported for model training.
Is there a built-in support for text classification?
Yes, Prodigy provides built-in recipes for text classification.
What kind of models can be trained using Prodigy?
You can train machine learning models for NLP and computer vision tasks.
Do I need programming skills to use Prodigy?
Basic programming skills are helpful but not strictly necessary as Prodigy provides an easy-to-use interface.
Does Prodigy support custom recipes?
Yes, users can create their own custom recipes to fit specific annotation requirements.
Can multiple users collaborate on the same project?
Yes, Prodigy supports collaboration tools for multiple users to work on the same project.