- All AI Data
Store anything. Deploy anywhere. Fine-tune your own LLM models.
Loved by devs, trusted by enterprises
Trended #1 in Python
WHAT IS DEEP LAKE?
Not another vector database.
We support all AI data.
Generative AI may be new, but we’ve been building for this day for the past 5 years. Deep Lake is multi-modal, which means we support any AI data - and not just embeddings. Deep Lake combines the power of both Data Lakes & Vector Databases to build, fine-tune, & deploy enterprise-grade LLM solutions, & iteratively improve them over time.
Serverless Tensor Query EngineVector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop
Visualize & Version DataVisualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model
Stream Data to TrainingCompetitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained
How Deep Lake fits into your Large Language Model-based stack?
Loved by 100+ data teams and counting
“As the datasets enlarge and become multi-modal, next-gen solutions built specifically to address those use cases, like Deep Lake, will help AI teams deliver models to production faster, and more efficiently.”
CTO – Enterprise Analytics & AI, Head of Strategy – Enterprise & Cloud GroupIntel
“Downloading data every time you run an experiment is bound to break you and the training process. Deep Lake's on-the-fly streaming was an excellent choice for us: it was really easy to set up, and it started to bring the value from day one.”
Lead ML EngineerUbenwa AI
“Just needed to deploy a solution that works - and Activeloop made it simpler to ship our AI app quickly!”
Director, Machine LearningSDSC
“They started out with a vector store integration, so it's flown under the radar, but... @activeloopai's Deep Lake is an intriguing fully-fledged serverless data lake that supports attribute based filtering, multiple distance functions, MMR search.”
CEO & FounderLangChainAI
Incredible tool! One of our researchers at National Center for Supercomputing Applications had great success using Deep Lake for multimodal pipelining for self supervised video embeddings. We are now trying to move away from HDF5's as they are too slow, annoying to work with, and just don't have the features we need to pipe efficiently into PyTorch. Exciting!
“A 100x speedup of Tensor Query execution for semantic search and question answering on legal documents. Deep Lake’s minimalistic architecture provided flexibility and light touch installation for our customers without introducing complexity such as adding a microservice. With Deep Lake’s ultrafast data loader, PyTorch was able to natively access the data and distribute it automatically across MPI workers, allowing for highly parallel embedding search.”
“Davit & team are super responsive & hands-on with onboarding. Highly recommend the tool for managing large & complex datasets.”
“New models deployed in a matter of days instead of weeks.”
Director, Machine LearningIntelinAir
Your ML projects will never be dead in the water(If you use Deep Lake)
1import deeplake 2from PIL import Image 3 4ds = deeplake.load('hub://activeloop/mnist_train') 5 6# Display an image 7Image.fromarray(ds.images.numpy())
Visualize, query, version, & stream datasets
Deep Lake datasets are visualized right in your browser or Jupyter notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.
- Rapidly visualize different versions of your data
- Understand your data and improve its quality
- Query, train, & edit datasets with data lineage
- Evaluate model performance
Deep Lake Integrations
Deep Lake is revolutionizing Deep Learning. Dive into it.
Drive revenue growth by shipping AI products faster, saving money by saving on GPUs, increasing data scientists’ focus on core business problems, & eliminating failed ML project risk due to the lack of a solid data foundation.
> pip install deeplake
Deep Lake open source. Join the community
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