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Book synopsis

Stop fragile feature engineering workflows before they create inconsistent models, unreliable predictions, and costly deployment failures. Fix feature engineering challenges in your machine learning projects without relying on disconnected scripts, manual preprocessing, or hard to maintain workflows. This book gives you a practical implementation guide for building production ready feature engineering systems that scale from development to deployment not toy examples or theory heavy discussions. Learn to design automated pipelines, manage feature transformations consistently, and deploy reliable preprocessing frameworks using industry standard Python tools and proven engineering practices. Build reusable feature engineering pipelines that reduce development time and improve model consistency. Master production-focused workflows, automation strategies, and deployment ready preprocessing systems. Create scalable feature management solutions that support real world machine learning applications. Written for data scientists, machine learning engineers, AI developers, analytics professionals, and technical practitioners who need reliable feature engineering workflows in production environments. Based on practical implementation patterns, deployment challenges, and real world machine learning system design. You'll get exact techniques to automate feature engineering workflows, maintain data consistency, and deploy scalable preprocessing systems before small issues become production emergencies.

Book details

• Full title: Python Scripts for Feature Engineering
• Subtitle: Master Data Preparation: Pipelines, Production Systems, Automation and Real-World Deployment
• Author: Justin Benedict
• Language: English
• Binding: Paperback
• Number of pages: 306
• EAN: 9798181930599
• ID: 52901225
• Publisher: Independently published
• Weight: 715 g
• Dimensions: 280 × 216 × 16 mm
• Date of publishing: 16. June 2026

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