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Activeloop Secures $11 Million in Funding For AI Database
Activeloop, a California-based startup offering a specialized database to streamline AI projects, recently raised $11 million in series A funding. The investment came from Streamlined Ventures, Y Combinator, Samsung Next, and other investors. Founded by Davit Buniatyan, who left Princeton University to establish the company, Activeloop has distinguished itself by addressing the challenge of leveraging unstructured multimodal data for AI model training.
The Significance of Activeloop’s “Deep Lake” Technology
Activeloop’s “Deep Lake” technology, which enables AI applications to be created at a cost up to 75% lower than current market offerings while enhancing engineering teams’ productivity by up to five-fold, is becoming increasingly crucial as enterprises seek to exploit their complicated datasets for diverse AI applications. According to McKinsey research, generative AI has the potential to generate anywhere from $2.6 trillion to $4.4 trillion in global corporate profits annually across numerous sectors.
Training advanced foundation AI models entails dealing with massive unstructured data that includes text, audio, and video modalities. Activeloop’s Deep Lake standardizes this process by storing complex data in the form of ML-native mathematical representations (tensors), which can then be easily streamed to SQL-like Tensor Query Language or popular deep learning frameworks like PyTorch and TensorFlow, simplifying the model development process for developers.
Activeloop’s Functionality in Action
Notably, Activeloop’s Deep Lake improves upon conventional data lakes by transforming all data into tensor format, paving the way for seamless streaming to GPUs for training, ensuring no idle time for these powerful computational devices. Benefitting from open-source components like dataset format, version control, and extensive APIs for streaming and querying, Activeloop has made significant inroads in the enterprise space.
One of Activeloop’s customers, Bayer Radiology, utilized Deep Lake to consolidate diverse data modalities into a unified storage solution, thereby reducing data preprocessing time and enabling unique capabilities like querying scans in natural language. The platform’s knowledge retrieval feature enhances accuracy while lowering costs, appealing to industries such as biopharma, life sciences, medtech, automotive, and legal sectors.
Future Growth and Expansion Strategies
With the latest funding round, Activeloop aims to enhance its enterprise offering and expand its customer base. Additionally, the company plans to bolster its engineering team. The upcoming release of Deep Lake v4 promises features like faster concurrent IO, streamlined data loading for model training, and improved data lineage and external data source integrations.
Ultimately, Activeloop hopes to revolutionize enterprises’ data organization and retrieval processes, helping them save on costs associated with in-house solutions, manual labor, and traditional coding practices, thereby enhancing overall productivity and efficiency in the field of AI development.
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