  Traditional data lakes provide critical data infrastructure for analytical
workloads by enabling time travel, running SQL queries, ingesting data with
ACID transactions, and visualizing petabyte-scale datasets on cloud storage.
They allow organizations to break down data silos, unlock data-driven
decision-making, improve operational efficiency, and reduce costs. However, as
deep learning usage increases, traditional data lakes are not well-designed for
applications such as natural language processing (NLP), audio processing,
computer vision, and applications involving non-tabular datasets. This paper
presents Deep Lake, an open-source lakehouse for deep learning applications
developed at Activeloop. Deep Lake maintains the benefits of a vanilla data
lake with one key difference: it stores complex data, such as images, videos,
annotations, as well as tabular data, in the form of tensors and rapidly
streams the data over the network to (a) Tensor Query Language, (b) in-browser
visualization engine, or (c) deep learning frameworks without sacrificing GPU
utilization. Datasets stored in Deep Lake can be accessed from PyTorch,
TensorFlow, JAX, and integrate with numerous MLOps tools.
