NomadicML

🤖 Robot VLA training just got 100x easier. 🎥 Watch NomadicML auto-extract every single human motion for the same AI that Figure and Tesla are spending $$$ to achieve. Famous robotics companies like Figure ($1.7B raised) spend multi-millions building proprietary robot pre-training datasets. They know human demonstration data is the key to generalizable robots. But most teams can't afford dedicated ML engineers, custom annotation tools, and massive compute infrastructure just for data curation. That's where we come in and give EVERY robotics team Figure-level data capabilities: ✓ Extract complex motions: "picking up cup with right hand" ✓ Find edge cases: "recovering from dropped objects” ✓ Curate robot pre-training datasets from any human video Top autonomous robotics and vehicle companies already use NomadicML to curate their motion datasets. Because why manually label "human picks up box" 10,000 times when our AI can do it instantly? The playing field just got leveled. You don't need $$$ to train world-class robot policies anymore. View this specific batch as example: https://lnkd.in/gTqY5pHK Explore all our examples and start curating your own videos today for free: https://lnkd.in/gjQfaG7y

𝐀 𝐲𝐨𝐮𝐧𝐠 𝐭𝐞𝐚𝐦 𝐨𝐟 𝐩𝐢𝐨𝐧𝐞𝐞𝐫𝐬 𝐢𝐧 𝐕𝐢𝐬𝐮𝐚𝐥 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐒𝐲𝐬𝐭𝐞𝐦𝐬, NomadicML (PearX S24) 𝐡𝐚𝐬 𝐣𝐨𝐢𝐧𝐞𝐝 𝐭𝐡𝐞 Autoware Foundation 𝐚𝐬 𝐚𝐧 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐦𝐞𝐦𝐛𝐞𝐫! 🚗✨ NomadicML brings deep expertise in machine learning infrastructure — powering the processing of millions of hours of driving footage to enable scalable, end-to-end AI development. Their vision strongly aligns with Autoware’s mission, as they see tremendous synergy with our open-source approach to autonomous driving. As part of their membership, they commit to: 🔹 Integrate the NomadicML Platform and offer utility for intelligent data curation to automatically identify valuable training data 🔹 Provide dedicated resources and consultation to support Autoware’s AI-first, end-to-end model development ⚙️ In addition, at later stages of their engagement, NomadicML will help design and implement modern MLOps pipelines for efficient large-scale model training, enabling faster iteration and scalability for Autoware’s AI-first future. 💡 Here’s a good example of how NomadicML can be useful to the community: In a recent blog post, they demonstrated how their two-step pipeline transforms the open-source Honda Driving Dataset (HDD) from raw video into structured, training-ready datasets. This showcases how the NomadicML Platform can streamline data curation and accelerate model development. 🔗 Read the full post here: 𝘌𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵𝘭𝘺 𝘊𝘶𝘳𝘢𝘵𝘪𝘯𝘨 𝘝𝘪𝘥𝘦𝘰 𝘋𝘳𝘪𝘷𝘪𝘯𝘨 𝘋𝘢𝘵𝘢𝘴𝘦𝘵𝘴 𝘶𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘕𝘰𝘮𝘢𝘥𝘪𝘤𝘔𝘓 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮 https://lnkd.in/edAdKv2U NomadicML will initially participate in the Privately Owned Vehicle Working Group, while also exploring opportunities to contribute across other working groups as their journey with Autoware evolves. 🎉 We’re excited to have NomadicML on board and look forward to accelerating Autoware’s transition to an AI-first future together! 🚀💡 Visit NomadicML’s website to learn more about them ⤵️ https://www.nomadicml.com/ #Autoware #NewMember #MemberAnnouncement #OpenSource #AutonomousDriving #AI #MLOps #NomadicML Shinpei Kato Yang Zhang Suraj Gajendra Christian John Daisuke Tanaka Ryohsuke Mitsudome Johannes Betz Samet Kütük Muhammad Zain Khawaja Steve Taylor Rahul Mangharam Mustafa Bal Varun Krishnan

The gap between leaders and laggards in autonomous systems isn't compute or data volume anymore. It's who can transform raw footage into intelligence fastest. (📹 🔄 🧐 🤖) ➡️ 💪🧠 We put NomadicML (PearX S24) to the test on Honda Research Institute USA, Inc.'s open driving dataset and boosted a sample pedestrian detection VLA model by 35% using only 500 minutes of video with also: - 300+ ego-vehicle turns auto-annotated with precise reasoning - 90% accuracy in 50 training steps (not 5000) - Minutes (not months) to production-ready datasets See the full technical breakdown by Akshay Gopalkrishnan and Varun Krishnan → https://lnkd.in/gkebrnjE While your team manually labels footage, others are shipping models that actually understand rare motion events and edge cases that matter. They stopped drowning in millions of hours of operational footage and started using NomadicML to curate AI training data. NomadicML is the only video platform that enables true curation and reasoning capable of analyzing and identifying complex motion in your videos. Start curating your own datasets today at https://www.nomadicml.com/ #AutonomousDriving #ComputerVision #DatasetCuration #AITraining #ADAS #DeepLearning #VideoAnalysis #MachineLearning #AutonomousVehicles #EdgeCases #MLOps #DataAnnotation #PhysicalAI #AVDevelopment #AIAgents

“This is a co-pilot for my eyes!” 👀 “You can do that?!?” 🤯 “That’s it, video understanding is solved.” 🧠 These were some of the comments developers gave after seeing our video understanding demos. Big thanks to Demetrios Brinkmann and the MLOps community for hosting us. Special shoutout to our NomadicML (PearX S24) team members Egil Martinsson and Akshay Gopalkrishnan for braving hours and hours of in-person presentations. 😬 Interested in trying out our semantic video reasoning capabilities for yourself? Check out our NomadicML platform, link in comments!

Super stoked to announce that I’ve joined NomadicML (PearX S24) as Founding Machine Learning Engineer! Excited to advance the frontiers of multimodal AI alongside Mustafa Bal, Varun Krishnan and Akshay Gopalkrishnan