Development teams often embrace Agile ways of working, yet the systems we build can still struggle to adapt when business needs shift. In this talk, we’ll share the journey of how a cross-functional data science team at the LEGO Group evolved its machine learning architecture to handle real-world complexity and change.

We’ll highlight how new modelling strategies, advanced feature engineering, and modern MLOps pipelines were designed not only for performance, but for flexibility. You’ll gain insight into how we architected a resilient ML system that supports changing requirements, scales with ease, and enables faster iteration. Expect actionable ideas on how to future-proof your own ML solutions and ensure they remain relevant in dynamic business contexts.

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