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Autor(en): 
  • Justin Benedict
  • Python Scripts for Feature Engineering: Master Data Preparation: Pipelines, Production Systems, Automation and Real-World Deployment 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 14-24 Tagen versandfertig
    Veröffentlichung:  Juni 2026  
    Genre:  EDV / Informatik 
     
    COMPUTERS / Programming / Algorithms
    ISBN:  9798181930599 
    EAN-Code: 
    9798181930599 
    Verlag:  Independently Published 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 279 mm / B 216 mm / D 16 mm 
    Gewicht:  715 gr 
    Seiten:  306 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      
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