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  • 7th Annual
  • November 17-18, 2026
  • Austin, TX

AI Sovereignty

About the track

Running your own AI stack became an urgent question after the temporary deprecation of Anthropic’s Mythos, and it remains a poor fit for some organizations. This track covers the whole decision, not just the migration: which workloads are worth moving and which are not; what open-weight licenses actually permit, and where indemnification sits; how to prove capability was not lost, by replaying your own logged traffic against candidate models rather than trusting public benchmarks; and how to choose between self-hosting and a token factory, and where the cost curve genuinely crosses.

We will also cover risks, such as; benchmark contamination, dependence on labs subject to other jurisdictions, serving economics propped up by cheap capital, and the alignment and censorship decisions already baked into weights. Sessions span C-level, legal, platform, and AI/ML, because no single function owns the whole path. It’s built for product leads, AI/ML leads, platform engineering and MLOps teams, ideally with at least one LLM workload in production or in pilot. Attendees leave with a transition plan for their own setup.

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Track host

Hannes Hapke

Director, Open Source, Dataiku

Hannes Hapke leads the 575 Lab at Dataiku, the company’s open-source office, where he drives initiatives around agents and LLMs. He is a four-time author whose books have become staples for production ML teams, including GenAI Design Patterns (O’Reilly, 2025), Machine Learning Production Systems (O’Reilly, 2024), Building Machine Learning Pipelines (O’Reilly, 2020), and NLP in Action (Manning, 2019). Previously, he was the first ML engineer at Digits, where he built the technical foundations for the company’s machine learning systems.
A Google Developer Expert and member of Google’s Developer Advisory Board, Hannes has been deeply involved in the production ML ecosystem for years, serving as TensorFlow Extended (TFX) community lead, moderating Google’s TFX Special Interest Group, and contributing to open-source projects like the Model Card Toolkit. His work centers on the architecture of production ML and generative AI systems: MLOps, continuous training, and the design patterns that make GenAI reliable at scale.

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Summit (2 days).

MLOps World | GenAI Summit 2026 is a two days of case studies, workshops, and expo on taking AI/ML and agentic systems into production – at the Etter-Harbin Alumni photo – full-bleed hero or browse files.

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