Activeloop
Multimodal AI data lake and vector store (Deep Lake)
About Activeloop
Activeloop develops Deep Lake, a database for AI that stores vectors, images, video and text in a lakehouse-style format and supports retrieval and model training. It offers managed and self-hosted options. It is used for multimodal retrieval and for streaming training data to GPUs.
Common use cases
- Multimodal RAG
- Computer vision datasets
- Model training data
- Semantic search
Products and services
Deep Lake
Multimodal vector store and data lake format.
Deep Memory
Retrieval accuracy improvement feature.
Key features
- Multimodal storage
- Vector search
- Dataset versioning
- Training data streaming
- Object-storage backed
- Python API
Integrations
- LangChain
- LlamaIndex
- PyTorch
- TensorFlow
- AWS S3
- OpenAI
Activeloop alternatives
FAQs
What is Deep Lake?
A format and database for storing and querying multimodal AI data.
Is it open source?
Parts of it are open source.
What is it used for?
Retrieval and training data management.
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