AUTOML IN ENTERPRISE: Best Practices & Limitations - Softcover

Inhaltsangabe

AutoML in Enterprise: Best Practices & Limitations is a practical executive guide to designing, governing, deploying, and scaling Automated Machine Learning (AutoML) across modern enterprises.

Rather than focusing only on algorithms, this book explains how successful organizations build enterprise-grade AI platforms that are secure, explainable, compliant, cost-effective, and operationally resilient. It bridges the gap between data science, enterprise architecture, MLOps, governance, and executive strategy.

Inside you'll learn how to:

• Build scalable enterprise AutoML architectures
• Design production-ready MLOps and AI operating models
• Improve data quality and feature engineering
• Govern AI with explainability, fairness, and accountability
• Secure AI platforms and meet regulatory requirements
• Manage model risk and production monitoring
• Compare commercial AutoML platforms with open-source ecosystems
• Measure ROI and optimize AI infrastructure costs
• Scale AI across large organizations
• Prepare for the next generation of Agentic AI and autonomous enterprise systems

Packed with architecture guidance, leadership insights, governance frameworks, and real-world enterprise projects, this book is ideal for organizations looking to move beyond experimentation and build trustworthy, production-ready AI capabilities.

Whether you're an Enterprise Architect, CTO, CIO, Chief AI Officer, Data Scientist, ML Engineer, MLOps Engineer, Technology Leader, Consultant, or Digital Transformation Executive, this book provides a practical roadmap for implementing AutoML at enterprise scale.

Build AI that organizations can trust—not just models that achieve high accuracy.

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