Articles liés à AUTOML IN ENTERPRISE: Best Practices & Limitations
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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AUTOML IN ENTERPRISE: Best Practices & Limitations
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Automl in Enterprise (Paperback)
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Paperback. Etat : new. Paperback. 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 architecturesDesign production-ready MLOps and AI operating modelsImprove data quality and feature engineeringGovern AI with explainability, fairness, and accountabilitySecure AI platforms and meet regulatory requirementsManage model risk and production monitoringCompare commercial AutoML platforms with open-source ecosystemsMeasure ROI and optimize AI infrastructure costsScale AI across large organizationsPrepare 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798184555386
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