XetHub is a collaborative storage platform that integrates Git-style workflows for managing machine learning assets, including models, embeddings, and code. It addresses the inefficiencies of traditional data management systems by providing scalable storage solutions tailored for Hugging Face users, enabling seamless collaboration among ML teams.
Funding
$7.5M 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.
MFounders
Product
Problem
Machine learning (ML) teams face challenges in managing and versioning large datasets, models, and embeddings, hindering collaboration and reproducibility. Traditional data management systems and Git-based solutions often lack the scalability and specialized features required for efficient ML asset handling. This leads to difficulties in tracking experiments, sharing data, and ensuring consistent results across different environments.
Solution
XetHub provides a collaborative storage platform that integrates Git-style version control for managing machine learning assets at scale. The platform is designed to address the specific needs of ML workflows, offering scalable storage and efficient collaboration features for datasets, models, and embeddings. By leveraging a unique backend storage solution, XetHub enables seamless collaboration among ML teams, simplifies experiment tracking, and ensures reproducibility. The platform integrates with Hugging Face, providing a streamlined experience for users of the popular ML platform.
Target Audience
XetHub primarily targets machine learning engineers, data scientists, and ML teams who require scalable storage and version control for their ML assets, particularly those using the Hugging Face ecosystem.
Features
- Git-style version control for machine learning assets, including datasets, models, and embeddings
- Scalable storage solution optimized for large ML datasets
- Integration with Hugging Face for seamless collaboration
- Support for experiment tracking and reproducibility
- Efficient data sharing and collaboration features for ML teams