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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.
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