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Feature Engineering Design Patterns for Machine Learning: Unlock Proven Pipelines and Scalable Solutions to Transform Raw Data into Predictive Power
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Publisher: Independently Published
Published: Sep 1 2025
Pages: 238
Weight: 0.92
Height: 0.50 Width: 7.00 Depth: 10.00
Language: English
Feature Engineering Design Patterns for Machine Learning: Unlock Proven Pipelines and Scalable Solutions to Transform Raw Data into Predictive Power
Struggling to turn messy data into reliable predictive features that survive production realities? Feature Engineering Design Patterns for Machine Learning offers a practical, pattern-first playbook for building feature pipelines that scale, are auditable, and actually improve model performance.This book presents proven, production-ready design patterns for feature engineering: how to construct point-in-time correct temporal features, encode high-cardinality categories safely, build robust text and embedding features, automate feature factories, and integrate features into MLOps workflows. It focuses on concrete, repeatable engineering practices, templates, tests, and deployment strategies, that make feature work reproducible and low-risk.
What you'll get from this book
You will learn how to build feature engineering systems that are maintainable, explainable, and production-ready. Specifically, you will gain the skills to:
Create point-in-time, leakage-free feature materializations and backtests for realistic evaluation.
Encode categorical, temporal, and text signals at scale using target smoothing, hashing, EWMA, and embedding patterns.
Compose modular feature pipelines (pandas, scikit-learn, PySpark) and parameterize templates for rapid, safe feature generation.
Integrate features with feature stores, online serving, and CI/CD while versioning code, schemas, and artifacts.
Detect and respond to feature drift, establish monitoring and audit trails, and retire features with confidence.
Apply interpretability tools (SHAP, permutation importance) and selection strategies that balance performance, stability, and cost.
Packed with checklists, template code, and real-world blueprints from finance, retail, and customer support, this is a hands-on manual for ML engineers, data scientists, and software teams who need reliable feature pipelines, not theoretical fluff.
Ready to replace fragile experiments with repeatable engineering? Get your copy of Feature Engineering Design Patterns for Machine Learning and start turning raw data into durable predictive power today.
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