Predictive Analytics with Python

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.

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

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.

About the Technical Reviewer
Mayuri Dekate is a Technical Operations Manager at Amazon Web Services, where she leads a team of cloud support engineers across seven AWS analytics and AI services, including Amazon OpenSearch, Kinesis, MSK, QuickSight, Kendra, AppFlow, and Amazon Q. With deep expertise in cloud operations management and analytics support engineering, she focuses on building scalable support systems, improving customer experience, and driving operational excellence at enterprise scale.

Mayuri holds the Business Relationship Support Experience Owner designation for Amazon QuickSight and has conducted over 170 technical interviews as a certified interviewer. Her work bridges engineering rigor and product strategy, having contributed to supportability frameworks adopted across AWS analytics service teams.

Beyond her operational role, Mayuri is an active contributor to the technical community. She has published articles on platforms including DZone, Towards AWS, and Mind the Product, and serves as a reviewer as well as program committee member for conferences and award programs, including PEARC, ICML, and the SWE WE26 conference. She is also an IEEE Senior Member.

Mayuri holds AWS certifications including AI Practitioner and Solutions Architect Associate. She is passionate about mentoring engineers, advancing AI-driven support methodologies, and making cloud technology more accessible and reliable for organizations worldwide.