Ever tried building a credit risk model when your data lives in Google Sheets and your loan statuses are about as reliable as weather forecasts? You'll learn practical data science lessons about surviving data quality issues, the critical importance of target variable definition, adding genetics to feature selection algorithms, and how engineered transactional features can transform your predictions from probably fine to we actually know what we're doing. We’ll show how classical ML approaches like logistic regression and XGBoost remain highly effective for binary classification problems, proving that sometimes the fundamentals work better than the latest AI trends. Perfect for anyone who's ever wondered how machine learning works when your data isn't clean, your labels aren't perfect, and your stakeholders want results yesterday.

Do you often get asked about the newest GenAI use cases? Or maybe you've run into a puzzling Langchain error? If so, this session is for you. You'll see how at Mollie, we tackled these challenges by building our own framework GaaS (GenAI as a Service). We'll show you how developing an in-house GenAI platform speeds up development and streamlines AI adoption across teams. By building together concrete examples, you'll learn how a centralized REST API can make AI tools easy to use for everyone—giving each business unit a secure and efficient way to build their own AI-powered solutions. Whether you're just starting out or looking for real-world inspiration, you'll walk away with practical insights to boost your next AI project.

This presentation introduces the Genetic Algorithms + Feature Importance Feature Selection technique, implemented in the open source Python package felimination. Genetic algorithms are a powerful optimization technique that can be effectively utilized for feature selection in machine learning models. By combining genetic algorithms with feature importance, we aim to enhance the feature selection process, leading to more robust and interpretable models. We will start by reviewing genetic algorithms, detailing the steps of pool initialization, crossover, mutation, and selection. The presentation will continue by showcasing some code snippets using felimination, a Python package containing a suite of algorithms for feature selection, including the genetic algorithm with feature importance selector. Claudio Salvatore Arcidiacono is a Senior Machine Learning Engineer at Mollie. He has been working in the fintech sector over the past 7 years with lots of experience in classical machine learning problems. He loves to contribute to data science open source libraries like feature engine, scikit-learn, and narwhals. He maintains a couple of open source libraries himself (felimination and sklearo). In his free time, he is a coffee scientist, using a data-driven approach to dial in the perfect cup of espresso.

In an era where everything even slightly related to generative AI is considered the new meta, it is hard to keep track of the technical increments that are actually useful to our work and domains. The announcement of the Model Context Protocol from Anthropic has generated a lot of buzz and is a good attempt to become the leader among the various LLM providers. This talk will take a stab at creating an overview and an honest take on what the MCP server will bring us and what it feels like to develop one for Mollie, trying to combine all emotions and experiences together to answer the question of whether it really lives up to the promise. Defying established presentation best practices, I will try to live-code a new MCP server providing functionality for a to-be-chosen service.