Sessions 2026
Machine Learning World Europe
17 - 18 November 2026 | Munich
Check out the first sessions below
More sessions will be published soon
17 - 18 November 2026 | Munich
Check out the first sessions below
More sessions will be published soon
Speaker/s:
Eduardo Sepulveda, ING BelgiumA Factory Approach to Churn Identification in Banking at ING
Summary:
Why it matters: In banking, churn must be identified at scale across multiple products, under strict regulatory constraints, using interpretable models despite severe class imbalance. What we cover: how an ML factory standardizes and accelerates churn deployment while ensuring explainability and governance. Takeaways: how banks can reduce time‑to‑production and deliver controlled, measurable business impact with ML.
Speaker/s:
Jyoti Yadav, MicrosoftJailbreaks, Filters and the Limits of Prompt Level Safety
Summary:
Everyone talks about jailbreaks and content filters but what stops LLM abuse when the filter fails? An attacker can make every single request look harmless while the overall pattern is clearly hostile. Drawing on a decade of hunting threat actors in cybersecurity, this session moves beyond prompt-level safety to behavioural, actor-level detection: spotting who is misusing a system, not just which prompt is bad. You will leave with a practical, layered way to think about AI safety.
Speaker/s:
Dr. Felix Reinhart, Miele XAI at Miele – From First AI Products to AI at Scale Across the Internet of Things
Summary:
How does Miele create customer value through AI? This talk shares key lessons learned on the journey from initial AI applications to scaling AI across a portfolio of connected appliances. It sheds light on strategic as well as technical aspects behind smart sensing, machine vision and LLM-based applications at Miele. The presentation highlights critical capabilities to deliver AI products for IoT appliances and sketches how large foundation models impact the innovation process itself.
Speaker/s:
Prince Tyagi, StatistaFrom Prompts to Systems: How Agentic AI Patterns Enable Reliable Data Workflows
Summary:
Most companies have data spread across databases, APIs, and reports. Answering a real business question means pulling from all of them at once. This session shows how AI agent patterns – Planner, Aggregator, and Verifier and turn that chaos into reliable answers. I will build a working demo live. Participants learn how to design agent systems, not just prompts. Key questions: When should humans review agent output? How do you handle conflicting sources? What makes an agent production-ready?
