Engineering ML & AI at Scale: Feature Selection, Explainability & Agentic Engineering
Mollie, Keizersgracht 126, 1015 CW Amsterdam, Netherlands · Amsterdam · Netherlands
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PyData Amsterdam is a vibrant community of Python and data enthusiasts that brings together this community and provides a forum for users and developers of open-source data tools.
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Engineering ML & AI at Scale: Feature Selection, Explainability & Agentic Engineering
PyData Amsterdam
Mollie
Amsterdam, Netherlands
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About 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—cov ering 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 a speaker
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 feature has a SHAP value of +1.40 builds little confidence. We show how we combine SHAP with AI to turn technical attributions into grounded, human-readable explanations analysts can act on immediately.
Talk 3: Reviewing the reviewer: bring rigour and discipline to your code base in the AI (slop) era, by the generating code is easier than ever. Agents make fewer mistakes, and merge requests are getting bigger. Does that mean the code is also getting better? The question of how to adapt as a software engineer to this rapidly changing set of tools is coming back more and more. It's one of the challenges I have been trying to tackle since joining Bonnie a year ago. I’m going to open-source my experience and share the different approaches I’ve taken over the last year to bring discipline and rigour to the way a fast-paced, engineering team ships features in an AI startup.
Direction
Mollie Amsterdam Office
📍Address: Keizersgracht 126, 1015 CW, Amsterdam
Mollie’s office is right in Amsterdam’s historic city center. It’s a nice ~20-minute walk (about 1.5 km) from Central Station. If you’d rather hop on public transport, trams 2, 17, or 24 will get you from Central Station to Keizersgracht in ~10 minutes.
Location
Mollie
Keizersgracht 126, 1015 CW Amsterdam, Netherlands
Mollie’s office is right in Amsterdam’s historic city center. It’s a nice ~20-minute walk (about 1.5 km) from Central Station. If you’d rather hop on public transport, trams 2, 17, or 24 will get you from Central Station to Keizersgracht in ~10 minutes.
Presented by a speaker
PyData Amsterdam is a vibrant community of Python and data enthusiasts that brings together this community and provides a forum for users and developers of open-source data tools.
Kally
85 Going
sinyo jawa swildens, Rutger van Baren