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Description

The Mozilla Data Collective serves as a platform aimed at transforming the AI-data landscape by prioritizing the needs of communities. It empowers data creators and caretakers to share their datasets according to their preferences while maintaining ownership and control over access and conditions. Users are able to upload datasets, select licenses—whether Creative Commons or custom options—define access guidelines, and stipulate requirements for compensation or acknowledgment, all while managing datasets as individuals, cooperatives, or trusts. This platform places a strong emphasis on ethical management, transparency, and community empowerment, standing in opposition to exploitative data extraction practices and fostering fairer participation. With a collection of over 300 high-quality datasets that are both created by and for communities, the platform spans a variety of applications, including multilingual speech-data collections. Additionally, it provides user-friendly tools, such as a public API, to facilitate the integration of these datasets into various applications, thereby enhancing accessibility and usability for developers. Ultimately, Mozilla Data Collective aims to create a more just and inclusive environment for data sharing and usage.

Description

Shaip is a comprehensive AI data platform delivering precise and ethical data collection, annotation, and de-identification services across text, audio, image, and video formats. Operating globally, Shaip collects data from more than 60 countries and offers an extensive catalog of off-the-shelf datasets for AI training, including 250,000 hours of physician audio and 30 million electronic health records. Their expert annotation teams apply industry-specific knowledge to provide accurate labeling for tasks such as image segmentation, object detection, and content moderation. The company supports multilingual conversational AI with over 70,000 hours of speech data in more than 60 languages and dialects. Shaip’s generative AI services use human-in-the-loop approaches to fine-tune models, optimizing for contextual accuracy and output quality. Data privacy and compliance are central, with HIPAA, GDPR, ISO, and SOC certifications guiding their de-identification processes. Shaip also provides a powerful platform for automated data validation and quality control. Their solutions empower businesses in healthcare, eCommerce, and beyond to accelerate AI development securely and efficiently.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Mozilla

Founded

2005

Country

United States

Website

datacollective.mozillafoundation.org

Vendor Details

Company Name

Shaip

Country

United States

Website

www.shaip.com

Product Features

Product Features

Data Labeling

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

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

Alternatives

Alternatives

Conseris Reviews

Conseris

Kuvio Creative