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

What if your AI didn't need a massive data centre to scale? A few months ago, we spoke with an organization that had big plans for AI. Their use case was powerful—predictive models that could save millions in operational costs. But there was a problem. Their reality: ❌ They didn’t have the budget for a massive data center. ❌ Centralizing their data wasn’t feasible due to privacy regulations. ❌ And hiring an army of data scientists? Not an option. They were stuck before they could even start. Here’s what we did: Instead of building costly infrastructure from scratch, we showed them how to: ❎ Use existing infrastructure—even across multiple locations. ❎ Analyze data where it already lives (no centralization required) with FederatedAI. ❎ Scale efficiently, step by step, without committing to massive upfront costs. The result? They launched their AI initiative in weeks, not months, and with a fraction of the resources they thought they’d need. With Data transfer cost <1% of their initial case, they were able to make this work. This isn’t just a one-off success story—it’s proof that scaling AI doesn’t have to be reserved for companies with massive budgets. With distributed, privacy-preserving systems, you can scale AI to fit your organization—not the other way around. What is holding you back from scaling AI? Happy to explore how to make it work for you. #AI #CostEfficiency #FederatedLearning #ScalableAI

The shiny promise of AI is everywhere. But behind the scenes? Too many enterprises are stuck in outdated processes: 📍 Moving data between systems, creating duplication and delays. 📍Centralizing data, even when it’s unnecessary or unsustainable. 📍Harmonizing formats, wasting time instead of driving value. The traditional way of managing enterprise data isn’t just inefficient – it’s expensive, complex, and unsustainable. Modern businesses need a new approach: one that keeps data native, processing local, and collaboration simple. This is how we turn this vision into reality 👉 Keep data where it is – we eliminate unnecessary data movement. 👉 Process data at the source – fast, efficient, and secure. 👉 Work with native formats – skip the harmonization headaches. 👉 Collaborate on scattered data – no matter the owner or location. Our lean, distributed architecture ensures businesses can generate insights from multiple systems, sites, and formats – while data owners retain full control. No duplication. No silos. Just actionable insights that drive real outcomes. 💡 Let’s stop wasting resources and start generating real value. Connect with us to learn how Katulu can help you get there. #DataInnovation #EnterpriseAI #Efficiency #DigitalTransformation

👋 So your data is scattered in different places - systems, geographies - making it really hard to find and use all the information needed to create accurate models? We've built a little something to help you tie everything together: 🚀 Introducing Katulu's Unified Data Catalogue 🚀 For around 90% of today's data, value creation hinges on efficiently leveraging data across distributed systems while upholding privacy and compliance standards. Our Unified Data Catalogue provides a single, cohesive view of all available datasets across your organization’s distributed environment. Imagine being able to securely 👉 access 👉 discover 👉 organize data from disparate sources—without moving or duplicating it. By centralizing only metadata and governance information, our Unified Data Catalogue enables quick discovery and compliant usage of datasets while keeping the actual data decentralized 🌎 🔍 Key Benefits: - Seamless Data Discovery: Quickly identify and access datasets across departments and regions, empowering data scientists and engineers to know what’s available to enrich their models. - Enhanced Collaboration: Facilitate secure data sharing with context, enabling teams to work together on AI initiatives with a clear understanding of what data can be leveraged. - Operational Efficiency: By centralizing data visibility, we reduce redundancy and streamline the path from data discovery to actionable insight. With the Unified Data Catalogue, Teams can now confidently access and align data assets across distributed systems—creating new possibilities for value, faster insights, and innovation without compromising security or compliance. If you'd like to try our Unified Data Catalogue to quickly locate, assess, and use distributed data for model-building, reach out to learn more! #DataScience #FederatedLearning #AI #DataCatalogue #Innovation #Privacy

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