Organize complex unstructured data. Train AI models. Retrieve knowledge with AI. 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.

Deep Lake maintains the benefits of a vanilla data lake, such as time traveling, SQL queries, ingesting data with ACID transactions, & visualizing terabyte-scale datasets. Deep Lake comes with one key difference. With Deep Lake, multi-modal complex data, such as images, audio, videos, annotations, & tabular data is stored as tensors and rapidly streamed to (a) query, (b) in-browser visualization engine, or (c) ML models without sacrificing GPU utilization.

Deep Lake datasets, including the embeddings, are visualized right in your browser or Jupyter notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on-the-fly, & stream them to your LLM of choice for fine-tuning. Powerful query features to curate subsets in natural language. Tensor Query Engine allows you to query complex data fast and materialize it on-the-fly for subsequent training. Use familiar SQL syntax or natural language - chat with multi-modal data, at scale