In a tutorial on fraud detection using declarative machine learning, the low-code AI platform Predibase and the open-source Ludwig project are utilized to build an end-to-end model for identifying fraudulent credit card transactions. The dataset, derived from European transactions in September 2013, highlights the challenge of class imbalance, as fraudulent instances are a mere 0.172% of the total. Predibase simplifies the modeling process by offering a range of suggested models, including neural networks and LightGBM, which cater to different data types and imbalances. The tutorial demonstrates the use of configuration-driven model training, enabling users to experiment with various architectures without extensive coding knowledge, and emphasizes the importance of precision and recall in evaluating model effectiveness. Predibase's approach, leveraging the Encoder-Combiner-Decoder (ECD) architecture and LightGBM, provides a robust foundation for handling imbalanced datasets and offers tools for further refinement, such as upsampling, downsampling, and adjusting class weights, all within a unified interface.