In this implementation, we will construct the foundational elements of a credit card fraud detection system leveraging SageMaker. Our approach commences with the training of an anomaly detection algorithm, followed by the training of two XGBoost models for supervised learning. Given the inherent data imbalance prevalent in fraud detection scenarios, the first model employs data re-weighting, while the second model adopts data re-sampling techniques, specifically leveraging the popular SMOTE technique to augment the representation of rare fraud cases.
Our comprehensive solution also encompasses a practical demonstration of invoking a REST API to replicate a real-world deployment scenario. AWS Lambda serves as the trigger for both the anomaly detection and XGBoost models. To execute the entire workflow seamlessly, you can select "Run->Run All" from the menu within Studio, or alternatively, "Cell->Run All" when operating within a SageMaker Notebook Instance.