Activeloop

Activeloop

软件开发

Mountain View,California 5,224 位关注者

Deep Lake: Database for AI

关于我们

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications (https://github.com/activeloopai/deeplake; 8K+ stars). It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in one place. Deep Lake is used by Intel, Matterport, Hercules.ai, Red Cross, Yale, & Oxford. Try out Deep Lake today via app.activeloop.ai Activeloop's founding team is from Princeton, Stanford, Google, and Tesla, and is backed by Y Combinator.

所属行业
软件开发
规模
11-50 人
总部
Mountain View,California
类型
私人持股
创立
2018
领域
Data Science、AI、Artificial Intelligence、Data pipelines、Cloud computing、Machine Learning、Computer Vision、Generative AI、Vector Search、LLMs和Large Language Models

地点

  • 主要

    196 Castro St

    US,California,Mountain View,94041

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Activeloop员工

动态

  • 查看Activeloop的公司主页,图片

    5,224 位关注者

    Large Language Models are transforming biotech. Learn how Flagship Pioneering is streamlining scientific research with highly-accurate RAG with Activeloop Deep Lake & 4th Gen Xeon Scalable Processors.

    查看Davit Buniatyan的档案,图片

    Deep Lake | Database for AI

    Drug Discovery, meet Generative AI. Thrilled to unveil Flagship Pioneering's exciting achievement in the biotech sector: harnessing the power of Retrieval Augmented Generation (RAG) for faster drug discovery and improving retrieval accuracy by 18.5% with Activeloop Deep Lake. Flagship Pioneering is a biotechnology company that invents platforms and builds companies focused on making bigger leaps in human health and sustainability. The company’s work ranges from applications in human health, such as designing new therapeutic modalities or early cancer detection, to tackling challenges in climate and sustainability, such as finding more resilient forms of agriculture. Flagship, and its Pioneering Intelligence (PI) initiative, as well as Activeloop embarked on a collaboration to solve a challenge: efficiently answering complex scientific questions by searching through large-scale, multi-modal data without compromising accuracy or adding complexity? This is where Activeloop made a difference. Together, Pioneering Intelligence and Activeloop formed a research partnership to address these needs. PI developed systems to generate and evaluate “realistic” questions across a diverse range of biological topics that Flagship might pose during scientific exploration. Activeloop provided Deep Lake, the database for AI, and a capability called Deep Memory. Deep Memory enhances retrieval accuracy using a learnable index from labeled queries for specific RAG applications without affecting search time. ?? With Activeloop, Flagship Pioneering significantly improved their retrieval capabilities, with a 18% increase in accuracy compared to traditional methods, and streamlining drug discovery R&D process. ?? Key Insights: 1?? Efficiency & Accuracy: Deep Lake and Deep Memory enhance RAG applications in biotech with simpler data access and increased accuracy. 2?? Multi-Layer Solutions to Solve Pressing Issues: The synergy between Flagship Pioneering, Activeloop, and the Intel Rise Program showcases how biotech's most pressing challenges can be addressed with AI-native data storage and computing (Intel XEON scalable processors). 3?? Multi-Modality as a Key to Innovation: With plans to expand across data types, Flagship is leading industry innovation in how biologists interact with scientific data ?? Mark Kim summarized it best while talking about the collaboration and Deep Lake as a key building block for GenAI: In science, sometimes you have to rethink the basics to make progress. Flagship’s work with Activeloop has been all about that—getting back to the core of how we store and retrieve data for AI to speed up how we solve really tough scientific problems. This success wouldn't be possible without the support of Chris, Susan, and Arijit from the Intel Corporation & the thought leadership of Ian and Mark from Pioneering Intelligence, who are at the forefront of GenAI at Flagship Pioneering! Read the case study (in the comments) and watch the video below.

  • Activeloop转发了

    查看Davit Buniatyan的档案,图片

    Deep Lake | Database for AI

    Activeloop's multi-modal database is critical for underwriting policies supported by complex data for insurance. This helps to reduce the risk and claims losses when customer claims are filed. Last week was honored to be on a panel at #InsureTechNY lead by Vas Bhandarkar from ScoreData Corporation who introduced Insurance GenAI Stack along with Vibhanshu Abhishek (Alltius), Alex Babin (HerculesAI), Thanasis "Thani" Delistathis, Varadarajan Rajaram.

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  • Activeloop转发了

    查看Alison Granger Ganansia的档案,图片

    Event Logistics Manager

    Join us tonight at the AI For Developers #20 Speakers Kobie Crawford?| Developer Advocate -?Databricks Davit Buniatyan?| Founder -?Activeloop Ivan ?? Nardini Nardini?| Developer Relations Engineer -?Google An evening exploring AI workflow optimization, scalable AI-powered search systems, and the transformative advancements of the Gemini platform. Network, learn, and connect with the AI community in SF AI User Group Doors open at 6 pm!

  • Activeloop转发了

    查看The Midway SF的公司主页,图片

    1,337 位关注者

    The Midway was honored to host Activeloop's RetrieveX AI Conference, an exclusive event for innovators building high-accuracy, multimodal workflows. The conference brought together leaders from Microsoft, Meta AI, Bayer Radiology, Y Combinator, Flagship Pioneering, Lepton AI, Expanso, Omneky, Cresta, as well as the co-creators of Chameleon, PyTorch, CAFFE, and Kubeflow. Attendees engaged with 15+ industry pioneers from Fortune 500 companies and leading research institutions, exploring how to create highly accurate, LLM-powered solutions that drive real business value. Carefully curated sessions featured key insights on optimizing RAG for multi-modality and precision, leveraging object storage for scalable GenAI systems, implementing data flywheels to enhance GenAI accuracy, and striking the balance between speed and accuracy in large-scale AI deployments. https://lnkd.in/ekAKux7m #AIConference #MachineLearning #ArtificialIntelligence #RetrieveX #ActiveLoop #TheMidway #MLResearch #AITrends

  • Activeloop转发了

    查看Emanuele Fenocchi的档案,图片

    Machine Learning Engineer

    What an amazing conference! On October 17th in San Francisco, RetrieveX brought together top minds from the world’s leading tech companies to showcase innovative projects. At the event, Davit Buniatyan, CEO and Founder of Activeloop, introduced Deep Lake 4.0, which now also features a multi-modal retrieval approach powered by ColPali’s late interaction mechanism. These enhancements significantly advance document retrieval by utilizing both textual and visual data, addressing some of the toughest challenges in AI data retrieval, such as accuracy, cost-efficiency, and scalability. I'm incredibly proud to have been part of the team that developed and deployed these powerful new features. Additionally, I had the opportunity to present two real-world use cases with Vivek Gangasani from AWS, showing how Deep Lake and AWS together provide a winning combination. Key highlights of Deep Lake 4.0 include: ?? Index-on-the-Lake: Enables sub-second queries directly from any object storage, across clouds. ?? 10x Cost Efficiency: Reduces the need for costly in-memory storage and large clusters, offering a more scalable solution. ?? High Accuracy: Utilize multiple indexes (embedding with quantization, lexical, inverted, etc.) for rapid search on object storage with minimal caching, ready for neural search technologies like ColPali. ?? True Multi-Modality: Supports diverse data types with enriched metadata. ?? Enhanced Performance: Delivers up to 10x faster read/write speeds due to optimizations in low-level code. If you're looking to experience the next generation of AI data retrieval, now's the time to explore the power of Deep Lake 4.0. Blog post: https://lnkd.in/d2v3dSG8

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  • 查看Activeloop的公司主页,图片

    5,224 位关注者

    Davit Buniatyan unveiled Deep Lake 4.0, the new major release of Deep Lake with 200+ AI Data Leaders from companies like Bayer, Tesla, Ally Financial, LinkedIn, Adobe, Walmart, Netflix, DoorDash, RingCentral, Zendesk and more attending. It was a blast! AI data retrieval systems today face 3 challenges: limited modalities, inaccuracy, and high costs at scale. Deep Lake 4.0 fixes this via true multi-modality, higher accuracy, and slashing query costs by 2-10x with our unique index-on-the-lake technology. The attendees heard from AI thought leaders from a lot of great companies and creators of Albumentations, CAFFE, PyTorch, and Kubeflow. There was networking, good food, and even... demos of our product in Tesla Cybertrucks! If you're having FOMO from the missed the event, check out the reel below!

    查看Davit Buniatyan的档案,图片

    Deep Lake | Database for AI

    We've Launched Deep Lake 4.0 at RetrieveX on October 17th, with 200+ AI Data Leaders from companies like Bayer, Tesla, Ally Financial, LinkedIn, Adobe, Walmart, Netflix, DoorDash, RingCentral, Zendesk and more attending. It was a blast! AI data retrieval systems today face 3 challenges: limited modalities, inaccuracy, and high costs at scale. Deep Lake 4.0 fixes this via true multi-modality, higher accuracy, and slashing query costs by 2-10x with our unique index-on-the-lake technology. But also, the conference was packed with amazing talks from: Armen Aghajanyan from AI at Meta/FAIR presenting his learnings from training Meta Chameleon and his thoughts on the future of multi-modal LLMs and RAG. Rob Ferguson, Head of AI, Microsoft for Startups presenting who and why leads in retrieval augmentation. David Aronchick, CEO Expanso and Co-Creator of Kubeflow and Bacalhau presenting on executing one's models wherever your data and users are. Steffen Vogler, Bayer, presenting the next-gen approach to AI in Healthcare. Ian Trase, Flagship Pioneering, presenting how the company's Flagship Pioneering arm makes big leaps in scientific research with GenAI. Yangqing Jia, Founder Lepton AI and co-creator of PyTorch and CAFFE, presenting on building enterprise-ready LLMs. Diana Hu, Group Partner at Y Combinator, moderating a panel on scaling efficiently in the age of GenAI with HerculesAI CTO Gevorg Karapetyan and Omneky CEO Hikari Senju. Sazzadur Rahman, Spotter, presenting optimizing content creation on YouTube for contextual search with up to billion-scale search. Vladimir Iglovikov, CEO and Creator of Albumentations, presenting efficient data augmentation in vision AI during his practical workshop. Vivek Gangasani, Amazon Web Services (AWS), presenting how to use Bedrock for GenAI and combine it with Activeloop Deep Lake to improve Retrieval Kelly Peng, Kura founder, presenting on building multi-modal AI that sees, remembers, generates. Bill Sun, Generative Alpha, presenting AI trader with reasoning capabilities powered with GenAI. Our team with Sasun Hambardzumyan and Mikayel H. presenting sub-second, multi-modal AI search on object storage. Major thanks to our sponsors at Amazon Web Services (AWS), Intel Corporation, and Microsoft for supporting our vision and the event!

  • Activeloop转发了

    查看Amirhossein Azami的档案,图片

    Ph.D. Candidate in Electrical Engineering at UT Dallas

    ?? Introducing My New RAG-based Chatbot with Deep Lake, LangChain, GPT-3.5 Turbo, and Streamlit! ???? As a researcher, I’ve often wished I could ask questions directly to the text files of research papers I’m reading, effortlessly extracting the specific information I need. Unfortunately, PDF readers don’t offer this kind of interactive experience, so I decided to build my own tool to solve the problem. This chatbot leverages Retrieval-Augmented Generation (RAG) — a framework that combines retrieval techniques with generative language models. In RAG, the system retrieves the most relevant chunks from a document and feeds them to the language model, ensuring that the response is accurate and grounded in real data. This makes it perfect for research, where context and precision are key. The chatbot transforms large documents into vector embeddings stored in Deep Lake. When you ask a question, the system uses cosine similarity to find the most relevant chunk and feeds it to GPT-3.5 Turbo to generate a context-aware answer. And the whole experience becomes more accessible and engaging with Streamlit’s graphical interface! ?? Key Technologies: Deep Lake by Activeloop for managing vector databases LangChain for LLM orchestration and seamless integration GPT-3.5 Turbo by OpenAI for generating responses Streamlit for building an intuitive and user-friendly front-end ?? GitHub Repository: https://lnkd.in/gq-4s4X7 Special thanks to Tom Yeh for his AI by Hand series and Pavan Belagatti for his insightful RAG guide on Medium. Your work was a tremendous help in bringing this project to life! If you’ve ever wanted a smarter way to interact with your documents, this tool might be just what you need! #AI #NLP #LLM #RAG #Chatbot #LangChain #DeepLake #Activeloop #Streamlit #GPT3 #MachineLearning #ResearchTools #GenerativeAI

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  • Activeloop转发了

    查看Gevorg Karapetyan的档案,图片

    CTO & Co-Founder @ HerculesAI | Product and Technology

    I had the pleasure of being part of an insightful panel on #Scaling #GenAI #Efficiently at the #RetrieveX #Conference, organized by Activeloop It was a great experience sharing the stage with Y Combinator Partner, Diana Hu as the moderator, and my co-panelist Hikari Senju, CEO at Omneky. I had the chance to discuss how our clients are successfully scaling their GenAI initiatives with us across their organizations. Also one of the topics we delved into was how traditional digital transformation is increasingly being replaced by business reinvention, as companies reimagine themselves in the age of AI. It was an exciting conversation overall! Big thanks to Activeloop folks for organizing this fantastic event. We're proud to be their clients, powering our retrieval pipelines with their powerful Vector DB #HerculesAI #businessReinvention #genAI

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  • Activeloop转发了

    查看Steffen Vogler的档案,图片

    Principal Data Scientist at Bayer

    I've just experienced it firsthand: San Francisco's tech ecosystem amazes with its unparalleled collaborative spirit ?? At RetrieveX, I had the privilege of presenting new concepts in GenAI-enabled data and knowledge collaboration for Digital Health to Bay Area founders and developers. The discussions were deeply inspiring, especially when diving into the details of Bayer's upcoming AI Innovation Platform (you folks really know how to build successful products ?? ). The Bay Area's warm welcome has been nothing short of extraordinary. A heartfelt thank you to the entire Activeloop team for orchestrating such a phenomenal event as RetrieveX. PS: I love the pic with the Waymo robo taxi next to the historic tram. This really embodies SF. To my pals in Germany: there is no driver in the Waymo ?? - can you believe this ?? this shakes the core of my German heritage (j/k I don't even own a car) #HealthcareInnovation #TeamBayer #BayerInRadiology #SeeThePossibleCreateTheFuture #AI #LifeScience #HealthForAll #HungerForNone #Bayer Bayer | Pharmaceuticals

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