Machine Learning Feature Store Book – Example Projects (github.com)

A new repository has been launched showcasing three complete machine learning systems that highlight various architectural patterns and use cases, particularly focusing on the integration of feature stores. These projects aim to provide best practices for building production-quality ML systems, including a Titanic survival prediction, air quality forecasting, and credit card fraud detection, all utilizing XGBoost as the modeling backbone. The repository is structured for easy onboarding, guiding users through setup and execution using a command-line interface. This initiative is significant for the AI/ML community as it serves as an educational resource, illustrating the practical application of feature stores and best practices in ML pipelines. Each example not only demonstrates batch and real-time prediction capabilities but also emphasizes the importance of features in model training and inference. The inclusion of interactive dashboards for results visualization enhances usability and user engagement, while potential enhancements like LLM capabilities for natural queries expand the projects' functionality, making them valuable for both learning and deployment in real-world scenarios.