1 research outputs found
Deep Lake: a Lakehouse for Deep Learning
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 takes over common analytical workflows, traditional data lakes
become less useful 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