Activeloop's Deep Lake is revolutionizing the way AI models are trained and deployed. This enterprise-grade database is designed to handle complex, unstructured data, including videos, text, images, PDFs, vectors, and more. With Deep Lake, users can organize and manage their data more efficiently, cutting data preparation times by up to 50%. The platform's advanced visualization and data lineage features allow for effortless querying and curation of datasets using natural language.

Deep Lake also excels in knowledge retrieval, offering up to 22.5% more accurate results for Retrieval-Augmented Generation (RAG) systems. It learns from user query history to optimize index usage, ensuring highly accurate retrieval across millions of documents. Security is a top priority, with SOC 2 Type 2 certification and a strong commitment to data security and operational excellence.

Trusted by Fortune 500 enterprises and trending #1 on GitHub, Deep Lake is a powerful tool for anyone looking to streamline their AI data management and accelerate model training. Whether you're fine-tuning large language models or preparing data for machine learning, Deep Lake provides the robust infrastructure needed to drive innovation and efficiency in AI.

Organize Complex Unstructured Data

Deep Lake maintains the benefits of a vanilla data lake while storing multi-modal complex data as tensors. This allows for rapid streaming to query, visualization, or ML models without sacrificing GPU utilization. This feature is crucial for managing large volumes of unstructured data efficiently, enabling faster data processing and model training.

Train AI Models

Deep Lake standardizes how you train ML models and store your ML data, cutting data prep times by 50%. This feature includes advanced visualization and data lineage features, making it easier to prepare and manage data for model training. By reducing data prep times, Deep Lake allows data scientists to focus more on model development and less on data wrangling.

Retrieve Knowledge with AI

Deep Lake enhances knowledge retrieval accuracy by up to 22.5% for RAG (Retrieval-Augmented Generation). It learns which indexes to use for user queries based on prior history, improving the accuracy of AI-driven knowledge retrieval systems. This feature is essential for building highly accurate knowledge retrieval systems across tens of millions of documents, making it easier for users to find relevant information quickly.

Data Preparation Efficiency

Deep Lake reduces data prep times by 50% and includes advanced visualization and data lineage features. This feature allows for effortless querying and curation of datasets using natural language, making it easier to prepare data for model training. By speeding up the data preparation process, Deep Lake enables data scientists to iterate faster and improve model performance.

Tensor Database for Complex, Unstructured Data

Deep Lake maintains the benefits of a vanilla data lake while storing multi-modal complex data as tensors. This allows for rapid streaming to query, visualization, or ML models without sacrificing GPU utilization. This feature is crucial for managing large volumes of unstructured data efficiently, enabling faster data processing and model training.

Serverless Tensor Query Engine

Deep Lake's Serverless Tensor Query Engine allows for serverless query of multi-modal data, including embeddings or metadata. This feature enables fast and efficient data retrieval, making it easier to access and use data for model training and other AI applications.

Visualize and Version Data

Deep Lake enables visualization and understanding of data, as well as tracking and comparing versions over time. This feature allows users to visualize and understand their data, as well as the embeddings, and track changes to improve data and model quality. By providing version control, Deep Lake helps users manage and maintain the integrity of their data over time.

Stream Data to Training

Deep Lake efficiently streams data from remote storage to GPUs as models are trained. This feature optimizes GPU utilization and reduces training time, making it easier to fine-tune large language models on custom datasets. By streamlining the data streaming process, Deep Lake allows users to train models faster and more efficiently.