Predictive Analytics with Python - Build Production-Ready Predictive Models with Polars, Pandera, Scikit-Learn, XGBoost, MLflow, FastAPI, Docker, and Python (English Edition) #1006612

Build Models That Survive Beyond the Notebook.

Book Description
Data 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 Contents
1. From Notebooks to Systems
2. The Modern Python Environment
3. High-Performance ETL with Polars
4. Defensive Data Programming with Pandera
5. Feature Engineering as Software
6. Handling Real-World Messiness
7. The Baseline: Linear Pipelines
8. Productionizing Gradient Boosting (XGBoost)
9. The Tuning Lifecycle and Experiment Tracking
10. Model Evaluation and Interpretation
11. Engineering Time-Series Features
12. Modern Forecasting with Nixtla
13. The Deployment Gap: Serialization and Packaging
14. Serving Predictions with APIs
15. Monitoring and Model Governance
16. Capstone: Building the Enterprise AVM
       Index