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• date: 11 October 2026
• posted by: BeMyLove

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.

Who is this book for?
This book is tailored for intermediate data analysts, business intelligence developers, and aspiring ML engineers who want to move beyond notebooks and build reliable, automated predictive systems. Junior data scientists seeking practical software engineering skills to operationalize models in production will also benefit significantly.

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.



   
   
   

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