Build Models That Survive Beyond the Notebook.
Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com.
● Production-first data pipeline engineering using Polars for high-performance ETL and Pandera for data contracts.
● Full MLOps lifecycle coverage with CRISP-DM, Scikit-Learn, XGBoost, MLflow experiment tracking, and hyperparameter tuning.
● Enterprise-grade capstone project building an Automated Valuation Model deployed with Docker and FastAPI.
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.
● Build resilient predictive pipelines using Scikit-Learn and XGBoost with production patterns.
● Automate hyperparameter tuning and track model experiments using MLflow effectively.
● Deploy production predictive models as REST APIs using Docker and FastAPI.
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
About the Author
Rahul Kumar Thatikonda is an Analytics Manager and Business Transformation Leader. He specializes in bridging experimental notebook code with production-grade software to upgrade critical industrial infrastructure. Rahul is dedicated to building resilient AI systems that deliver transformative, societal value.
