Katulu GmbH

𝗗𝗼 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 “𝗛𝗼𝘄 Siemens 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆 𝗺𝗮𝘀𝘁𝗲𝗿𝗲𝗱 𝗙𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗞𝗮𝘁𝘂𝗹𝘂?” 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗼𝘂𝗿 𝗻𝗲𝘄 𝘄𝗵𝗶𝘁𝗲𝗽𝗮𝗽𝗲𝗿 𝗼𝗻 𝗼𝘂𝗿 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗦𝗶𝗲𝗺𝗲𝗻𝘀. 👉 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗶𝘁 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 𝗮𝘁 katulu.io/en/#whitepaper 𝗮𝗻𝗱 https://lnkd.in/ggj-a7M3 👉 Learn how Siemens successfully implemented federated learning in two factories combining Katulu’s federated learning platform and Siemens Industrial Edge.  👉 Deep insights on challenges, experiences, and take-aways from a real-world implementation of federated learning in electronics manufacturing.  👉 And how Katulu and Siemens enable industrial-grade AI while ensuring privacy, data security, and compliance - plus deep-dive on architecture, technology stack and model performance results. 𝗠𝗲𝗲𝘁 𝘂𝘀 𝗮𝘁 𝘁𝗵𝗲 #𝗦𝗣𝗦, 𝗷𝗼𝗶𝗻𝘁𝗹𝘆 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗶𝗻𝗴 𝗼𝗻 𝗧𝗵𝘂𝗿𝘀𝗱𝗮𝘆, 𝗡𝗼𝘃 𝟭𝟲𝘁𝗵 𝗶𝗻 𝗵𝗮𝗹𝗹 𝟭𝟭 𝗼𝗻 𝘀𝘁𝗮𝗴𝗲 𝗮𝘁 𝟭𝟬:𝟯𝟴𝗵 𝗮𝘀 𝘄𝗲𝗹𝗹 𝗮𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗦𝗶𝗲𝗺𝗲𝗻𝘀 𝘀𝘁𝗮𝗿𝘁-𝘂𝗽 𝗰𝗼𝗿𝗻𝗲𝗿 𝗶𝗻 𝗵𝗮𝗹𝗹 𝟭𝟭. Please share your feedback and questions in a comment. #electronicsmanufacturing #industrialAI #federatedlearning #IndustrialEdge #Siemens Orlando Hohmeier Anne Mareike Schlinkert Michael Kuehne-Schlinkert Boris Scharinger Erik Schwulera Konstantin Schmidt Thomas Blumauer-Hiessl Michael Gepp Franz Delcuve Tam Erdt

Have you seen Data Scientists secretly wear Red Capes? They should! In semiconductor manufacturing, AI/ML can decrease manufacturing costs by up to 17% (Source: McKinsey). Within the next two to three years, this means $35-$40 billion in value could be generated. A couple of barriers need to be dealt with before, though, for learning across fabs - International Regulations impacting Data Sharing Across Country Borders - TBs of Data from Heterogeneous Equipment 👇 Before rolling up your sleeves, make sure to read how to deal with this

How to ensure your AI in Manufacturing delivers better results faster? Working together has many benefits - and with the right technology at the core, it even keeps your data invisible from those you work with 👇Kudzai Manditereza is capturing the essence of the mechanism that keeps your data sovereign in his talk with Michael

Looking to optimize manufacturing processes with seemingly contradictory needs? ❌ Data-Privacy vs. Collaborative Insights ❌ High-Precision vs. Compliance ❌ Global Scalability vs. Real-Time Insights 🤝 Meet Federated AI - This decentralised approach to training and operating AI changes how - and WHERE - we put data to use. 👇 As explained for #Semiconductor Manufacturing

𝗕𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗔𝗜 𝘁𝗼 𝘁𝗵𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝘁𝗵𝗮𝘁 𝗣𝗼𝘄𝗲𝗿𝘀 𝗔𝗜 How can 𝗔𝗜 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 be deployed more efficiently to drive the production of the very chips that enable AI itself? Our latest article tackles how 𝗙𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗔𝗜 can solve the industry's biggest challenges—ensuring 𝗱𝗮𝘁𝗮 𝗽𝗿𝗶𝘃𝗮𝗰𝘆, boosting 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, and driving sustainability across global production lines. The very same technology that powers AI deserves the most cutting-edge tools to stay competitive. Curious to learn how the future of AI is being shaped in semiconductor fabs? Read the full article here 👇 #AI #FederatedAI #Semiconductors #DataPrivacy #Efficiency #Manufacturing #Innovation

Dezentrales maschinelles Lernen kompakt für die nächste Kaffee-Pause ☕ 🏅 Wie die Industrie mit Datensparsamkeit ihre Ziele erreicht Mit dem Leading Data & MLOps Experten Prof. Dr. René Brunner und Michael Kühne-Schlinkert 👇

🚀 Andrew Ng is buzzing about it, Apple is making privacy moves with it, and Gartner has crowned it at the top of their Hype Cycle. NVIDIA’s FLARE is showing use cases, and Flower is training the next wave of data scientists. 🔒 Federated Learning is a game-changer! The days of trying to centralize all our data are over—why should we? Privacy concerns and regulations demand that sensitive data stays local. FL lets AI models learn from decentralized datasets without sharing the data itself. From healthcare to finance to manufacturing, it’s revolutionizing industries, keeping data safe, and collaboration strong. 💰 But there’s more—FL can cut data transfer costs by keeping the data on the edge and only centralizing model weights. That’s smarter, faster, and more cost-effective. ✨ For data scientists still writing their own code before tackling distributed data, we’ve got something to simplify your life: move from prototype to production in just 3 easy steps. 🌍 Welcome to the September of Scalability! 🌍 👉 Curious how? Follow us to learn about bringing federated learning into production and unlocking next-level value! #AI #FederatedLearning #DataPrivacy #Scalability #AIAlliance #AndrewNg #Apple #Gartner

We’re excited to share that Katulu has been recognized as an IDC Innovator in AI, alongside Xplain Data GmbH and OmegaLambdaTec GmbH! 🚀 Thank you to Jan Burian for the shoutout and to Siemens for the fantastic ‘AI with Purpose’ Summit. To celebrate, we’re giving away a special surprise to the first 5 data scientists who follow us! 🕵️♂️ Curious? It’s something that will supercharge your AI toolkit. Follow us and be among the first to find out! #FederatedLearning #IDC #Katulu #DataScience #AIwithPurpose

While the world is busy debating how AI adoption is actually progressing, we at Katulu have been hard at work in our lab, getting AI systems up and running. And here’s what we’ve learned: the real challenge isn’t AI—it’s the data. 🎓 Most real-world data is spread across the globe, tangled in different formats, and protected by strict privacy regulations like GDPR, CCPA, and export control laws. Whether you're in finance, healthcare, electronics manufacturing, semiconductors, telecommunications, or cybersecurity, data is your most valuable asset—and your biggest headache. Here’s What We Mean Across Industries: 💡Electronics & Semiconductor Manufacturing (Zero-Defect Production): In manufacturing, achieving zero-defect production is the holy grail. But when you’re restricted by export control laws that apply to your production data, collaborating across borders becomes a legal quagmire. Imagine if you could improve your yield globally while keeping all your sensitive production data exactly where it needs to be. 🔐 Cybersecurity (Pattern Detection): Cybersecurity firms are all about spotting patterns in data to prevent attacks. But when customer data is tightly controlled and can’t be shared, detecting those patterns becomes a challenge. How powerful would it be to detect threats across multiple clients without compromising their data security? 💰Finance (Fraud Detection): Banks and financial institutions are constantly battling fraud. The catch? Privacy regulations make it nearly impossible to share transaction data across borders or even within different branches. What if there was a way to detect fraudulent patterns without needing to centralize all that sensitive data? 🏥 Healthcare (Speeding Up Recovery): In healthcare, speeding up patient recovery could be significantly improved if hospitals could leverage insights from other institutions’ data. But privacy laws prevent the direct sharing of patient information. What if hospitals could collaborate and improve patient outcomes without ever sharing personal data? Rethink How You Use Data With Us So, here’s the big idea: Stop worrying about centralizing your data. Imagine having a cockpit where you can orchestrate AI training centrally, but send your AI out to work right where the data lives. No moving, no copying—just efficient, secure problem-solving at the source. Let’s rethink how data can truly support your business objectives. That's when adoption is 🚀 Ready to rethink how you use data? Let’s get started. #RealWorldData #AI #Katulu #DataPrivacy #Innovation #GDPR #Cybersecurity #Finance #Healthcare #Manufacturing Stay Tuned for More Insights We're excited to share more insights into working with data in its natural habitat. We'll dive deeper into real-world examples and provide practical advice for both data science and business enthusiasts. If you are looking to bridge the AI adoption gap, there’s something for everyone. Keep following us for the latest updates! 🎯

This is what happens when Industrial AI Pioneers leave the Lab At #HannoverMesse we got Global Manufacturers excited for our Secure, Compliant and Efficient way of working with data. 🤫 Our secret: Raw data never leaves the factory, is never stored by us and remains invisible to partners. It still super performant and allows accurate & precise predictions for Predictive Quality Control: Our federated data platform shares models trained on manufacturing defects and quality metrics without actually sharing the underlying data. This drives quality standards and quick identification of production issues - across your manufacturing network. Value Chain Optimization: Our platform helps improve efficiency without compromising the confidentiality of data from different stakeholders. Companies can collaborate on optimizing end product quality and production by sharing insights derived from decentralized data sources. All raw, sensitive data stays with their owners. Predictive Maintenance: When working with customers, most prefer not to share data from their shop floors with you. With our federated data platform, you can predict equipment failures and schedule maintenance without exposing their sensitive operational data. This can significantly reduce downtime and improve asset utilization across different facilities. Design and Simulation: In design and testing, our federated data platform allows companies to collaboratively improve product designs and simulations without sharing the proprietary design data. This is crucial in competitive fields where design secrecy is paramount. R&D Collaboration: Research and development often requires huge datasets to validate new technologies. We allow for collaborative R&D efforts across companies and regions while ensuring that each party's data remains private and secure. Compliance and Security: Our federated data platform provides a way to harness the power of big data while complying with Export Control regulations, GDPR or HIPAA. This is particularly relevant for multinational corporations that need to navigate the complex landscape of international data privacy laws. Edge Computing: With the growth of IoT devices in electronics manufacturing, federated data platforms can process data at the edge, i.e., close to where data is generated. This reduces latency, decreases the bandwidth needed for data transmission, and enhances the responsiveness of machine learning models. We are not done getting real world feedback for bridging Performance and Compliance in Industrial AI. For all those did not get a chance to speak to us at #HMI24 - What are your hurdles in making the most out of your data?