Prodigy AI Data Annotation Tool for Efficient Machine Learning Workflows
Prodigy is a machine teaching tool for efficient data annotation that integrates active learning and customizable workflows to accelerate machine learning model training.
- Best for
- Data Annotation for NLP
- Key capability
- Active Learning
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What is Prodigy?
Prodigy is a machine teaching tool designed to accelerate the process of data annotation for machine learning projects. It enables users to create custom labeling interfaces and workflows that integrate seamlessly with active learning models, allowing for faster and more efficient data labeling. Prodigy supports a variety of data types including text, images, and audio, making it versatile for many AI applications.
Key features of Prodigy
Prodigy offers interactive annotation interfaces, active learning integration, customizable workflows, and direct model training capabilities. It is designed to reduce the time and cost of creating high-quality labeled datasets for machine learning.
Active Learning
Prioritize data samples that maximize model improvement, reducing annotation effort.
Custom Recipes
Create flexible annotation workflows to handle diverse data types and tasks.
Multi-Modal Support
Annotate text, images, audio, and video data within a single platform.
Model-in-the-Loop
Integrate model predictions to assist and speed up annotation.
API Access
Programmatically control annotation projects and integrate with other tools.
Pros and cons of Prodigy
Pros
- Highly customizable annotation workflows
- Supports multiple data types including text and images
- Integrates active learning to reduce labeling effort
- Enables iterative model training within the tool
Cons
- Requires some programming knowledge to create custom recipes
- Pricing may be high for small teams or individual users
Key use cases for Prodigy
Data Annotation for NLP
Label text data efficiently for natural language processing tasks such as named entity recognition, text classification, and intent detection.
Image and Video Annotation
Annotate images and videos for computer vision projects including object detection and image classification.
Active Learning Integration
Use active learning to prioritize data samples that improve model training with fewer annotations.
Customizable Annotation Workflows
Create tailored annotation interfaces and workflows to fit specific project requirements.
Rapid Model Training
Iteratively train and improve machine learning models directly within the annotation environment.
How Prodigy works
- 1
Install and Set Up
Install Prodigy on your local machine or server and configure it for your data type.
- 2
Create Annotation Recipes
Define custom annotation workflows (recipes) tailored to your project needs.
- 3
Annotate Data
Use the interactive interface to label data efficiently, with suggestions from active learning models.
- 4
Train Models
Train or update machine learning models iteratively based on annotated data within Prodigy.
- 5
Export and Integrate
Export labeled datasets and models for use in production or further analysis.
Who is using Prodigy
Prodigy pricing
Personal
$390 USD per lifetime license (excl. tax)
For freelancers, indie developers and hobbyists: lifetime license with 12 months of free upgrades, basic HTTP authentication, community forum support and unlimited personal and professional use.
Company
$490 USD per seat, in packs of 5 seats (excl. tax), lifetime license
For startups, data science teams and enterprises: lifetime license with 12 months of free upgrades, HTTP auth and SSO with OpenID Connect, priority forum and email support, and seats transferrable within the company.
Plans and prices are as published by the vendor and can change. Check the official site before you buy. Open the pricing page (opens in a new tab)
Frequently asked questions about Prodigy
Prodigy supports annotation of text, images, audio, and video data.
Yes, Prodigy integrates active learning to prioritize the most informative samples for annotation.
No, Prodigy is a commercial product developed by Explosion AI.
Yes, Prodigy allows you to create custom annotation recipes tailored to your project.
This tool is designed to help users accomplish its core tasks more efficiently. It is typically used by individuals or teams looking to improve productivity and workflow.
Data handling and security practices vary by provider. Review the official privacy policy to understand how your data is stored and used.
Some tools offer a free plan or trial with limited features. Availability can vary, so confirm on the official website.
Yes, it can help with that use case depending on how you configure it and what features are available. You’ll get the best results with clear inputs and a defined goal.
Conclusion
Installation works like any other library, giving a standalone, self-hosted annotation back-end for Python 3.8+ on macOS, Linux and Windows. Built-in recipes cover tasks such as named entity recognition, text classification, computer vision, audio and video annotation and transcription, and the web application offers 23 annotation interfaces. Active learning models prioritize the most informative samples for annotation, and models can be trained from the annotations inside the tool. Fully custom data feeds and interfaces are defined as recipes, which is where some programming knowledge is required.
Because it works offline on your own hardware, no data ever needs to leave your servers. It is designed for machine learning engineers, data scientists and NLP researchers. Licenses are lifetime purchases with 12 months of free upgrades, sold as a Personal license or as Company seats in packs of 5, and hosted trials are offered to companies and organizations, not individuals.
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