Activeloop Deep Lake streamlines deep learning dataset management at scale. Deep Lake maintains the benefits of a vanilla data lake, such as time traveling, SQL queries, ingesting data with ACID transactions, and visualizing terabyte scale datasets for analytical workloads with one key difference. It enables the storage of complex data types such as image, video, audio data, annotations, or tabular data, as columns with native integration to deep learning frameworks.

As deep learning rapidly takes over traditional computational pipelines, storing datasets in a deep lake is becoming the de facto standard for shipping AI products fast. Deep Lake is optimized for cost efficient AI workflows. It allows rapid data streaming to deep learning frameworks over the network without sacrificing GPU utilization.

With Deep Lake, teams can build a solid data foundation and iterate fast on their AI products - without bottlenecks from low quality data, underutilized compute resources, and significant labor overhead required to build and maintain large amounts of data.

Deep Lake's features break down data silos, enable data driven decision making, improve operational efficiency, and reduce costs.