Übersicht			Auf mobile öffnen

Lieferstatus:	Vorankündigung
Veröffentlichung:	ANGEKÜNDIGT (Juli 2026)
Genre:	EDV / Informatik
	Artificial Intelligence / Artificial Intelligence (AI) / COMPUTERS / Artificial Intelligence / General / COMPUTERS / Business & Productivity Software / Business Intelligence / COMPUTERS / Management Information Systems / Distributed Training / Enterprise Software / KFServing/KServe / Kubernetes / Library, Archive & Information Management / machine learning / Machine Learning Pipelines / ML Operations / MLOps / Model Registry / Model Serving / Operational research / Pipeline orchestration / Platform Engineering mehr...
ISBN:	9780443452543
EAN-Code:	9780443452543
Verlag:	Elsevier
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 191 mm / D
Gewicht:	450 gr
Bewertung:	Keine Bewertung vor Veröffentlichung möglich.

Inhalt:

Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides solutions for real-world challenges. With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.