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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