Activeloop的封面图片
Activeloop

Activeloop

软件开发

Mountain View,California 5,568 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

    获取路线

Activeloop员工

动态

  • 查看Activeloop的组织主页

    5,568 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

    CEO @ Activeloop | YC S18 | Accurate AI Search on Your Data

    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Mayank Jain的档案

    Grow Your Product hunt Launch Upvotes | Software Developer ?? , AI Influencer?? Personal Branding| Brand Promotions|| 100K+ Linkedin Helping Brand to Grow ?? AI Promotion ??PH Hunter ?? 400+ PH launch successfully |

    ?????????????????????? ???????????????????? ???? ??????????: ???????? ???????????????? ?????? ?????? ????????, ???????????????? Imagine instantly connecting to and searching across all your data — PDFs, images, videos, structured data — no matter where it’s stored (S3, Dropbox, GCP). ? Use our Knowledge Agent, powered by AI search to scan up to billions of rows of any data - images, PDFs, text, tables and more, and provide a well-researched answer Deep Lake uses vision-language models to: ?? Search across multiple data types ?? Ingest multi-modal data without OCR Use cases: ?? Healthcare & Pharma: Accelerate research with, search across EHR data and medical papers for insurance claim processing, drug discovery, etc. ?? Finance: Analyze earnings reports + transcripts ?? Legal: Search patents, review contracts, and more Built from years of research at Princeton & refined through YC, Deep Lake makes multi-modal AI search smarter, faster, and more powerful than ever. Let’s chat if you want to see it in action! ???????????? ?????????????? ???? ???????? https://lnkd.in/gv67xS4t #AI #DataScience #DeepLearning #MultiModalAI #YCAlumni

  • 查看Activeloop的组织主页

    5,568 位关注者

    We are live on ProductHunt! Watch the in-depth demo of our AI Knowledge Agent and come ask questions on ProductHunt to support us.

    查看Davit Buniatyan的档案

    CEO @ Activeloop | YC S18 | Accurate AI Search on Your Data

    Our AI Knowledge Agent is live on Product Hunt. Conduct Deep Research on your data, no matter its modality, location, or size. Come celebrate our launch with us and ask any questions you might have! https://lnkd.in/gj8v37YF

  • Activeloop转发了

    查看Davit Buniatyan的档案

    CEO @ Activeloop | YC S18 | Accurate AI Search on Your Data

    The next chapter in the AI arms race isn't about models. It's about something more fundamental: Silicon. What we're seeing: AI companies are realizing they need to drive down costs by printing their own ASICs (Application-Specific Integrated Circuits) for their chips. Take Perplexity's recent move: ? They've partnered with Cerebras Systems and deployed their fine-tuned models on their chips ? Their benchmarks show up to 10-15X faster performance To make AI truly accessible and cost-effective, companies are discovering they need to own more of their stack. This vertical integration trend is just beginning. We've seen this pattern before: → Tesla did this with their own chips to build their supremacy in autonomous driving → Now, we're seeing more AI companies following a similar path The companies that can successfully execute this vertical integration - from models to silicon - will have a significant advantage in both performance and cost structure. This is a fundamental shift in how AI companies will need to operate to stay competitive.

  • 查看Activeloop的组织主页

    5,568 位关注者

    A week ago, we've launched our Ai Knowledge Agent. Today, we are excited to announce that we've been recognized as an Emerging Specialist in Generative AI Engineering and AI Knowledge Management Apps/General Productivity by @Gartner_inc. Even further, with the introduction of our AI Knowledge Agent that performs Deep Research on multi-modal private data, Gartner has placed us the closest to an Emerging Challenger compared to our peers in our quadrant. We consider our positioning in the Emerging Specialist quadrant by Gartner as confirmation of our mission to help answer tough questions on multi-modal data in highly complex cases - in drug discovery, financial analysis, medical claims processing, patent search, and beyond. To celebrate the inclusion - we're launching on @ProductHunt with an introductory offer! Click subscribe to be notified of the release and get exclusive, free access to Deep Lake Knowledge Agent! (link in next tweet) Subscribe to be notified on our Release in ProductHunt! https://lnkd.in/dtPZ5_eg Objectivity Disclaimer Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. From Innovation Guide for Generative AI Technologies, Generative AI Team, Gartner, Feb 2025

    • ai  knowledge management quadrant
    • ai engineering quadrant
  • Activeloop转发了

    查看Davit Buniatyan的档案

    CEO @ Activeloop | YC S18 | Accurate AI Search on Your Data

    I have a crazy theory about DeepSeek R1's efficiency that nobody has figured out yet. It’s different from another rumour that has been circulating. What if the secret isn’t about GPUs at all? We know that: ? DeepSeek R1 can run on CPUs ? You can get 6-7 tokens per second on 2 CPUs with 1TB RAM ? Cost: $6,000 per machine vs hundreds of thousands for a DGX box The rumour: DeepSeek team used low-cost Huawei GPUs. My hypothesis: The real efficiency could come from horizontal scaling on CPU machines. Take 10 machines ($60,000 total) instead of one DGX box. The key might be in DeepSeek R1's architecture: → Uses 256 experts → Each token prediction uses just 7-8 experts → Perfect for horizontal distribution Even top GPU inference providers haven’t figured out how DeepSeek achieves their performance. I can see 3 possibilities: ? Custom ASICs ? Optimized FPGA implementation ? CPU implementation with horizontal scaling Time will tell… What do you think?

  • Activeloop转发了

    查看Sally Ann Frank的档案

    Worldwide Lead @ Microsoft for Startups | Driving Digital Health Innovation | Speaker | Author

    ?? Drum roll, please! ?? Microsoft for Startups is pleased to announce our line-up of enterprise-ready startups featured at #HIMSS25. ?? Details on the portfolio here https://lnkd.in/es3egwBG and summary below. ?? PM me to arrange an in-person meeting at our booth, #2221. See you there! ?? Activeloop revolutionizing data management in the field of deep learning, offering a specialized data lake optimized for AI and machine learning applications. ?? Artisight offering a smart hospital platform that streamlines patient room and operating room workflows ?? BeeKeeperAI enabling algorithm owners and data stewards to collaborate using confidential computing and their EscrowAI product ?? CueZen driving behavior changes for better outcomes with hyper-personalized nudging ?? Ema Unlimited creating AI agents to streamline processes, while enabling non-technical users to build and deploy AI agents ?? Humata Health using AI to create frictionless prior authorization for providers, payers, and patients. ?? Kintsugi detecting signs of clinical depression and anxiety from short clips of free-form speech. ?? Octagos designing cutting-edge remote monitoring software and provides best-in-class service to healthcare professionals and patients, focusing on cardiac device management. ?? Opmed.ai improving case duration predictions through their AI-powered optimization engine ?? Outbound AI offering conversational AI built for healthcare, serving as workforce multipliers that drive productivity while improving the daily job experience for human talent. ?? RAAPID INC delivering a AI platform for end-to-end risk adjustment for accurate diagnosis, reduction in provider burden and optimization of revenue ?? Signal 1 offering a fully-integrated AI platform to speed adopt of safe and reliable AI solutions across a health system ?? Subsalt transforming privacy compliance from a legal process into a query-time automation by leveraging generative models to anonymize regulated enterprise data. ?? Toku using AI and retinal photography to enable accessible healthcare for early and accurate diagnosis of health conditions. ??Triomics providing a generative AI-powered platform for cancer care providers, helping to accelerate trial enrollment, quality improvement projects, and streamline healthcare operations. ??TruLite Health providing health equity solutions for health systems and hospitals, offering a point-of-care SaaS platform that provides health equity insights and patient-specific clinical and social data sets. ??VERITI Security, Inc. helping organizations maximize their security posture while ensuring business uptime, identifying and remediating potential risks and misconfigurations with safe remediation in one click. Tom Davis Patrick Lamplé Sylvie Atkins Andrea DeCamp Derek Haynes Bethany Cordes Tom Pauly Karla P. Microsoft for Healthcare David Rhew, M.D. #StartupSuccess #Healthcare #LifeSciences #Innovation #AIinHealthcare

    • 该图片无替代文字
  • 查看Activeloop的组织主页

    5,568 位关注者

    Deep Research on your own, multi-modal data? Yes, please - available now for everyone via chat.activeloop.ai!

    查看Davit Buniatyan的档案

    CEO @ Activeloop | YC S18 | Accurate AI Search on Your Data

    What if you could use OpenAI's Deep Research on your own, multi-modal data, with any model? Well, you can. Today, we're announcing Deep Research with Deep Lake Knowledge Agent. Why did we build this? Enterprises lose 21–25% productivity (roughly $20 million per year for a mid-size firm) on manual searches. Imagine paying your team to play hide-and-seek with your own data! Deep Lake fixes that. Unlike some other AI agents, Deep Lake's Knowledge Agent: 1. Is Multi-Modal: we use VLMs to process any data automatically, and query across it. 2. Available on Your Data: Deep Lake integrates both public & private data—deployable on your S3, GCP or Azure. Whether it’s internal reports, research, or proprietary IP, your data stays secure & compliant 3. Scales to Billions of Rows: with our index-on-the-lake technology, we're 10x more cost-efficient than in-memory workflows while super accurate. 4. Is Highly Accurate: We employ state-of-the-art of retrieval techniques, get full context from multi-modal data (like figures within PDFs), and learn from your previous searches to make our Knowledge Agent excel at your domain. 5. Works with Any Model: route to your favorite small or large model. Who uses this? Deep Lake knowledge agent is deployed with Fortune 500 companies and leading innovators like Flagship Pioneering, where it helped the company achieve faster scientific discovery with 18% more accurate retrieval. Try it out for free at chat.activeloop.ai More information in the launch article, as well as sample queries in comments:

相似主页

查看职位

融资