Labelbox operates a data training platform that utilizes AI-assisted labeling and a global network of experts to provide high-quality data curation and evaluation for machine learning applications. This platform addresses the challenge of efficiently managing large-scale data labeling and evaluation, enabling businesses to accelerate model development and improve AI performance.
Funding
$188.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SVFounders
Product
Problem
Training high-performing AI models requires massive amounts of accurately labeled data, which is often a time-consuming, expensive, and complex process. Existing data labeling solutions may lack the necessary tools for efficient data curation, quality control, and integration with model training workflows.
Solution
Labelbox provides a comprehensive data factory platform designed to streamline the entire AI development lifecycle, from data curation and labeling to model training and evaluation. The platform offers AI-assisted labeling tools, a global network of expert labelers, and robust quality assurance mechanisms to ensure high-quality training data. By integrating these capabilities into a unified environment, Labelbox enables AI teams to accelerate model development, improve AI performance, and reduce the overall cost of building AI applications. The platform supports both task-specific models and frontier models requiring reinforcement learning from human feedback (RLHF).
Target Audience
Labelbox targets AI teams, data scientists, machine learning engineers, and data labeling managers across various industries who are responsible for building and deploying AI models.
Features
- AI-assisted labeling tools for faster and more accurate data annotation
- Integrated data curation capabilities for efficient data selection and management
- Human-in-the-loop evaluation workflows for assessing model performance and identifying areas for improvement
- Support for various data types, including images, video, audio, and text
- Global network of expert labelers for specialized data annotation tasks
- Customizable quality control workflows to ensure data accuracy and consistency
- Direct integration with popular machine learning frameworks and cloud platforms
- RLHF platform with tooling and services powered by a global community of domain experts