Graviti
Manage unstructured data at scale to accelerate machine learning workflows
Target Audience
- Machine Learning Engineers
- Data Science Teams
- AI Development Companies
- Autonomous Vehicle Research Teams
Hashtags
Overview
Graviti helps AI teams organize and process complex unstructured data like images, videos, and sensor data. It streamlines the entire ML data pipeline from curation to version control and automation. The platform enables teams to collaborate effectively while maintaining data quality, ultimately reducing preparation time and improving model accuracy.
Key Features
Unified Data Hub
Central storage for raw data, metadata, and semantic labels
Git-like Versioning
Track dataset changes and collaborate through branches
Zero-Copy Curation
Create new datasets without duplicating source files
Quality Inspection
Identify imbalanced or low-quality data automatically
Training Automation
Trigger ML pipelines when new data gets added
Use Cases
Identify imbalanced datasets
Visualize version differences
Automate model training pipelines
Collaborate on shared datasets
Preprocess data at scale
Pros & Cons
Pros
- Specialized for unstructured ML data management
- End-to-end workflow automation capabilities
- Granular version control for datasets
- Enterprise-ready collaboration features
Cons
- Primarily targets ML teams (less useful for general analytics)
- Requires some technical ML workflow knowledge
Reviews for Graviti
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