Scaling Industrial AI Across Factories with Federated Learning: Michael Kuehne-Schlinkert - Katulu
AI in ManufacturingShow Notes
In this episode, I sat down with Michael Kuehne-Schlinkert, CEO of Katulu to discuss how Federated Machine Learning is transforming industrial AI. Here are some key takeaways: Federated Learning Enables Cross-Factory Collaboration Federated learning allows multiple factories to improve AI models without sharing sensitive data. By exchanging learnings, factories can build more robust models while maintaining data privacy and compliance. Collaboration on Model Training Without Compromising Privacy One of the biggest challenges in industrial AI is accessing the right data without compromising privacy. Federated learning addresses this by keeping sensitive data local, allowing companies to enhance their AI models collectively without exposing each other’s proprietary or sensitive information. Cost-Effective Scaling of AI Models Through Reuse Scaling AI across multiple factories typically involves high costs and complexity. Federated learning significantly reduces development, integration, and operation costs by allowing the reuse of models across different sites without duplicating efforts. Steamlined Development of Predictive Maintenance and Quality Control Models Federated learning helps streamline the development of ML models for predictive maintenance and quality control by aggregating insights from multiple sites, reducing the need for extensive data science expertise and making advanced AI accessible to more organizations Curious about how federated learning can scale industrial AI? Tune in to the full episode to learn more!
More from AI in Manufacturing
AI Is Reshaping Industrial Software. Who Wins? | Michael Finocchiaro Will AI simply make established industrial software better, or will it fundamentally change which companies dominate the market? In this episode of AI in Manufacturing, I sit down with Michael Finocchiaro, founder of ThreadMoat Intelligence, to explore how AI is reshaping the competitive landscape of industrial technology, where AI-native startups are challenging established vendors, and what the industrial software market could look like over the next five years. We go beyond the competitive landscape to examine how the underlying technology architecture is evolving, from increasingly composable software and AI-driven orchestration to the changing roles of traditional systems like MES and PLM. In this episode, we explore: Why AI-native startups are challenging established industrial software vendors and where incumbents still have an advantage. How AI is changing traditional industrial software categories, including MES, PLM, and manufacturing data platforms. Why composable architectures and AI agents could fundamentally change how industrial systems are integrated and orchestrated. How manufacturers should think about data ownership, interoperability, and vendor lock-in when evaluating emerging technologies. What the industrial software landscape could look like in five years, including the evolving roles of AI agents, knowledge graphs, and human decision-making. Michael brings a broad perspective from tracking industrial technology startups, interviewing founders, and analyzing emerging trends across engineering and manufacturing software. Whether you're building industrial software, investing in emerging technologies, or shaping your organization's manufacturing technology strategy, this conversation offers valuable perspectives on the shifts already underway and what they could mean for the industry. Learn more: Industry40.tv: https://www.industry40.tv
# AI in Manufacturing Podcast
# AI in Manufacturing Podcast — Show Notes ## Episode: Scaling Industrial Intelligence with the I3X Common API
# AI in Manufacturing Podcast: Episode Show Notes
**Podcast Name:** AI in Manufacturing Podcast **Episode Title:** How to Build AI Solutions That Actually Work on the Factory Floor **Guest:** Renan De Villiers, Founder & CEO, OSS Ventures **Host:** Kudzai Manditereza