GOTO - The Brightest Minds in Tech The AI Engineer's Guide to Surviving the EU AI Act • Larysa Visengeriyeva & Barbara Lampl
Jan 13, 2026
Larysa Visengeriyeva, a software engineer and author known as the 'godmother of MLOps', joins behavioral mathematician Barbara Lampl for an engaging discussion. They dive into the EU AI Act's implications for engineering practices and highlight that real compliance relies on strong MLOps and data governance, rather than just meeting legal requirements. Larysa shares practical frameworks like CRISP-ML and Machine Learning Canvas to simplify AI project management, stressing the importance of data quality and proactive engineering in building robust AI systems.
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Engineer Quality Before Legal Review
- Do build MLOps, documentation and governance proactively, not as an afterthought.
- Treat legal review as the final step after you engineered quality and governance into the system.
Booked Read On Flight To San Francisco
- Barbara read the book on a flight to San Francisco for a US client unrelated to the EU AI Act.
- She found the book useful for moving prototypes to production and scale.
Prioritise Data Quality First
- Do prioritise data quality as the foundation: format, normalization, error detection and correction.
- Invest time and engineering effort in data governance because model quality depends on it.
