Engineering ML & AI at Scale: Feature Selection, Explainability & Agentic Engineering

Inside the event

Calling all data scientists, machine learning engineers, and data enthusiasts! Join us on Wednesday, Oct 28 at Mollie. We're bringing together engineers from Mollie and Bonnie to share hard-earned lessons from the trenches—covering everything from feature selection and interpretable AI predictions to practical methods for navigating AI-generated code to help teams ship better software. Stick around after the talks for good food, drinks, and great conversations with fellow tech enthusiasts. 📋 Agenda 17:30 - 18:25: Welcome with food and drinks! 🍕🍺 18:25 - 18:30: Mollie’s Intro 18:30 - 19:00: Talk 1: Feature Selection in Production: Lessons from Fraud Detection, by Nicole Van de Weijer (Mollie) 19:00 - 19:35: Talk 2: Machine Learning Model Explainability in the AI Age, by Tim Haarman (Mollie) 19:35 - 19:45: Break ☕ 19:45 - 20:15: Talk 3: Reviewing the reviewer: bring rigour and discipline to your code base in the AI (slop) era, by Anne Lohmeijer (Bonnie) 20:15 - 21:00: Networking & Drinks 🎤 Talk Details Talk 1: Feature Selection in Production: Lessons from Fraud Detection, by Nicole Van de Weijer Managing engineered features in a production fraud model means trading off accuracy against latency and interpretability. In this talk, I'll walk through the feature selection options we considered for Mollie's consumer fraud detection model, comparing manual selection to automated methods, Backward Elimination (RFECV with SHAP or permutation importance) and Forward Selection (MRMR / MRMRCV), and their trade-offs. I'll close with the method we settled on and why. Talk 2: Machine Learning Model Explainability in the AI Age, by Tim Haarman Building an accurate model is only half the job: if users don't trust its predictions, they won't act on them. Understanding why a model made its choice is key to that trust. This is nothing new - we’ve had SHAP for years as a standard tool for explaining predictions - but it speaks the language of data scientists. Telling an analyst a fea