Activeloop is a data infrastructure company focused on enabling efficient handling of large-scale datasets used in deep learning and AI applications. Founded in 2018 and headquartered in the United States, the company addresses a core bottleneck in machine learning systems: managing, accessing, and scaling massive volumes of unstructured data such as images, videos, audio, and text. Activeloop is best known for its open-source dataset format and database called Deep Lake, which is designed specifically for AI workloads. The company’s tools are widely used in domains like computer vision, natural language processing, and autonomous systems, where datasets can reach terabyte or even petabyte scale. Its approach aligns with the broader shift towards data-centric AI, where the efficiency and quality of data pipelines are as important as model architecture.

From a technical perspective, Activeloop’s platform provides a unified data layer optimised for deep learning workflows. Deep Lake enables efficient storage, streaming, and versioning of large datasets, allowing models to access data directly from cloud storage without the need for complex preprocessing pipelines. It uses a tensor-based storage format that is optimised for GPU training, significantly improving data loading performance compared to traditional file-based systems. The platform supports dataset version control, collaboration, and reproducibility, making it easier for teams to track changes and experiment with different data configurations. Additionally, Activeloop integrates with popular machine learning frameworks such as PyTorch and TensorFlow, and supports vector search capabilities for embedding-based retrieval tasks. By simplifying data handling and enabling scalable, high-performance data pipelines, Activeloop helps developers and organisations build and deploy AI systems more efficiently.