Librería Samer Atenea
Kálamo Books
Librería Elías (Asturias)
Librería Kolima (Madrid)
Librería Proteo (Málaga)
Build Models That Survive Beyond the Notebook.Book DescriptionData Science Finds the Signal. Engineering Turns It into Business Value.Moving a model from a Jupyter Notebook to a production system requires engineering discipline, not just data science skills. Predictive Analytics with Python is the definitive guide for the engineering-first era of data science, helping you transition from fragile notebook workflows to resilient, production-ready predictive systems built for real-world infrastructure.You begin by replacing slow legacy workflows with a modern technical stack, high-performance ETL with Polars, data contract enforcement with Pandera, and resilient Scikit-Learn and XGBoost pipelines with rigorous feature engineering, cross-validation, and experiment tracking using MLflow. The book then advances into time-series forecasting with Nixtla before covering model serialisation, REST API deployment with FastAPI, Docker containerisation, and production monitoring as well as governance.The book culminates in an end-to-end capstone project building an enterprise-grade Automated Real Estate Valuation Model. By the end, you will engineer predictive systems that prioritize stability, auditability, and transformative business value.What you will learn● Transition fragile notebook workflows into robust production-grade software engineering practices.● Execute high-performance ETL and data processing using the Polars library at scale.● Enforce rigorous data contracts using Pandera to validate pipeline inputs automatically.Table of Contents1. From Notebooks to Systems2. The Modern Python Environment3. High-Performance ETL with Polars4. Defensive Data Programming with Pandera5. Feature Engineering as Software6. Handling Real-World Messiness7. The Baseline: Linear Pipelines8. Productionizing Gradient Boosting (XGBoost)9. The Tuning Lifecycle and Experiment Tracking10. Model Evaluation and Interpretation11. Engineering Time-Series Features12. Modern Forecasting with Nixtla13. The Deployment Gap: Serialization and Packaging14. Serving Predictions with APIs15. Monitoring and Model Governance16. Capstone: Building the Enterprise AVM Index
