Graviti provides a data management platform that enables efficient acquisition, storage, and processing of unstructured data through features like data version control and workflow automation. This technology enhances productivity and scalability for machine learning projects by streamlining data curation and collaboration across teams.
Funding
$10M 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.
Founders
Product
Problem
Machine learning projects often struggle with the complexities of managing unstructured data, leading to inefficiencies in data curation, version control, and team collaboration. Traditional methods lack the necessary tools for efficient data acquisition, storage, and processing, hindering productivity and scalability.
Solution
Graviti offers a data platform designed to streamline the management of unstructured data for machine learning and business analytics. The platform provides tools for data hosting, allowing users to manage raw data, semantics, and metadata in datasets on the cloud. It also features Git-like data versioning, enabling users to track data changes and collaborate across teams. Furthermore, Graviti allows users to automate data pipelines, process large data volumes, and build automated workflows.
Target Audience
Graviti's primary customers are machine learning engineers, data scientists, and business analysts working with unstructured data in various industries.
Features
- Centralized data hosting for managing raw data, semantics, and metadata in the cloud.
- Git-like data versioning for tracking changes and enabling team collaboration.
- Workflow automation tools for building and automating data pipelines.
- Customizable filters for querying and processing data.
- Identification of imbalanced data to improve data collection strategies.
- Data quality inspection to spot and correct errors in semantic data and metadata.
- Automated data preprocessing, including data augmentation and auto-labeling.
- Automated training pipelines triggered by new data additions.
- Visualization of differences between data versions.
- Enterprise-grade access control and logging for data governance.