We provide a simple API for creating, storing, versioning, and collaborating on multi-modal AI datasets of any size. With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that?

GitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal.

Our founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights.

Activeloop is hiring, and we want you! Check out our open roles on our YC page and join the fun.

10-min demo: https://activeloop.wistia.com/medias/aibvo0dst2 Whitepaper: https://www.deeplake.ai/whitepaper

APPLY HERE!

We are building Deep Lake, the Data Lake for Deep Learning https://github.com/activeloopai/deeplake

The landscape of computation resources across different special hardware and cloud providers is becoming increasingly fragmented.

We're building a platform that unifies and abstracts away infrastructure for easier and highly efficient machine learning and deep learning.