AUTOML IN ENTERPRISE: Best Practices & Limitations

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 systemsPacked 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.