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.
Résultats de recherche pour AUTOML IN ENTERPRISE: Best Practices & Limitations
Image d'archives
AUTOML IN ENTERPRISE: Best Practices & Limitations
impression à la demandeVendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur I-9798184555386
Acheter neuf
EUR 26,78
Expédition à EUR 2,69
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
Automl in Enterprise (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
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 multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798184555386
Acheter neuf
EUR 29,54
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
AUTOML IN ENTERPRISE
impression à la demandeVendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9798184555386
Acheter neuf
EUR 31,02
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
AUTOML IN ENTERPRISE
impression à la demandeVendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9798184555386
Acheter neuf
EUR 26,32
Expédition à EUR 6
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
Automl in Enterprise (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
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
Acheter neuf
EUR 30,75
Expédition à EUR 44,20
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
AUTOML IN ENTERPRISE : Best Practices & Limitations
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware - 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. N° de réf. du vendeur 9798184555386
Acheter neuf
EUR 41,36
Expédition à EUR 35
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)