2 repository-uri
Storage repositories optimized for organizing and retrieving diverse data types like audio, images, and text.
Distinct from Data Lakes: Distinct from Data Lakes: specifically handles multimodal AI data types optimized for deep learning.
Explore 2 awesome GitHub repositories matching data & databases · Multimodal. Refine with filters or upvote what's useful.
Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea
Functions as a multimodal AI data lake that organizes diverse data types into a unified storage format.
DeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines. The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search tha
Manages multimodal AI data types optimized for deep learning using lazy loading to prevent memory overflow.