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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.