GOTO - The Brightest Minds in Tech
The GOTO podcast seeks out the brightest and boldest ideas from language creators and the world's leading experts in software development in the form of interviews and conference talks. Tune in to get the inspiration you need to bring in new technologies or gain extra evidence to support your software development plan.
GOTO - The Brightest Minds in Tech
The AI Engineer's Guide to Surviving the EU AI Act • Larysa Visengeriyeva & Barbara Lampl
This interview was recorded for the GOTO Book Club.
http://gotopia.tech/bookclub
Check out more here:
https://gotopia.tech/episodes/409
Dr. Larysa Visengeriyeva - Author of "The AI Engineer’s Guide to Surviving the EU AI Act" & Independent Consultant for EU AI Act Engineering
Barbara Lampl - Behavioral Mathematician at empathic business by Barbara Lampl
RESOURCES
Larysa
https://x.com/visenger
https://bsky.app/profile/visenger.bsky.social
https://github.com/visenger
https://www.linkedin.com/in/larysavisenger
Barbara
https://x.com/BarbaraLampl
https://www.linkedin.com/in/barbaralampl
https://barbara-lampl.tumblr.com
Links
https://ml-ops.org
https://github.com/visenger/awesome-mlops
https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
https://machinelearningcanvas.com
https://louisdorard.gumroad.com/l/mlcanvas
https://ml-ops.org/content/crisp-ml
DESCRIPTION
Barbara Lampl interviews Larysa Visengeriyeva, software engineer and "godmother of MLOps", about her new book on AI engineering and compliance. What starts as a discussion about the EU AI Act quickly reveals a deeper truth: the real challenge isn't regulatory compliance - it's fundamental engineering practices.
Larysa argues that quality AI systems require robust MLOps, comprehensive documentation, and proper data governance, whether regulation mandates it or not. Drawing from frameworks like CRISP-ML and the Machine Learning Canvas, the book provides practical checklists and methodologies for taking AI projects from prototype to production. Written partially in Ukraine during wartime, this "battle-tested" guide addresses the gap between technical and non-technical stakeholders, offering a common language for building sustainable AI systems.
RECOMMENDED BOOKS
Larysa Visengeriyeva • The AI Engineer's Guide to Surviving the EU AI Act • https://amzn.to/42SKOuU
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/4ox4Eos
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Diana Montalion • Learning Systems Thinking • https://amzn.to/3ZpycdJ
Bernd Rücker • Practical Process Automation • https://amzn.to/3cs3BSH
Lauren Maffeo • Designing Data Governance from the Ground Up • https://amzn.to/3QhIlnV
Katharine Jarmul • Practical Data Privacy • https://amzn.to/46XPrns
Zhamak Dehghani • Data Mesh • https://amzn.to/3tTCwAC
Kate Stanley & Mickael Maison • Kafka Connect • https://amzn.to/40Jq5Jz
Serious Conversations About Real AI Research; decoding the ArXiv into your headphones.
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