KFServing on Kubernetes (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"KFServing on Kubernetes" "KFServing on Kubernetes" is a comprehensive guide for deploying, managing, and scaling machine learning models in modern, cloud-native environments. The book begins with a comparative analysis of model serving paradigms, highlighting how KFServing distinguishes itself within the ML lifecycle and seamlessly integrates with the Kubeflow ecosystem. Through detailed chapters on Kubernetes fundamentals and advanced cloud-native design patterns, readers gain foundational knowledge while appreciating the interplay between model management frameworks such as Seldon Core, Ben...
"KFServing on Kubernetes"
"KFServing on Kubernetes" is a comprehensive guide for deploying, managing, and scaling machine learning models in modern, cloud-native environments. The book begins with a comparative analysis of model serving paradigms, highlighting how KFServing distinguishes itself within the ML lifecycle and seamlessly integrates with the Kubeflow ecosystem. Through detailed chapters on Kubernetes fundamentals and advanced cloud-native design patterns, readers gain foundational knowledge while appreciating the interplay between model management frameworks such as Seldon Core, BentoML, and others.
Delving into the heart of KFServing, the book meticulously unpacks its core architecture-including the InferenceService custom resource, advanced subresources for transformation and explainability, and Knative integration for robust autoscaling and revision management. Readers learn to design secure, high-availability deployments with production-grade configurations, leverage automated installation techniques, and implement best practices for multi-tenancy, resource governance, and storage backend integration. Specialized content addresses model versioning, advanced traffic routing, and multi-endpoint orchestration, equipping practitioners to handle even the most demanding real-world scenarios.
Beyond deployment fundamentals, "KFServing on Kubernetes" addresses critical operational concerns: performance tuning with hardware accelerators, advanced resource scheduling, comprehensive security with mTLS and RBAC, and end-to-end observability with industry-standard monitoring tools. The text concludes with forward-looking insights on extensibility, integrating CI/CD MLOps workflows, developing custom components, and navigating multi-cluster or edge deployments. Clear, practical, and up-to-date, this resource empowers ML engineers, architects, and platform teams to deliver resilient, scalable, and explainable ML services at enterprise scale.
"KFServing on Kubernetes" is a comprehensive guide for deploying, managing, and scaling machine learning models in modern, cloud-native environments. The book begins with a comparative analysis of model serving paradigms, highlighting how KFServing distinguishes itself within the ML lifecycle and seamlessly integrates with the Kubeflow ecosystem. Through detailed chapters on Kubernetes fundamentals and advanced cloud-native design patterns, readers gain foundational knowledge while appreciating the interplay between model management frameworks such as Seldon Core, BentoML, and others.
Delving into the heart of KFServing, the book meticulously unpacks its core architecture-including the InferenceService custom resource, advanced subresources for transformation and explainability, and Knative integration for robust autoscaling and revision management. Readers learn to design secure, high-availability deployments with production-grade configurations, leverage automated installation techniques, and implement best practices for multi-tenancy, resource governance, and storage backend integration. Specialized content addresses model versioning, advanced traffic routing, and multi-endpoint orchestration, equipping practitioners to handle even the most demanding real-world scenarios.
Beyond deployment fundamentals, "KFServing on Kubernetes" addresses critical operational concerns: performance tuning with hardware accelerators, advanced resource scheduling, comprehensive security with mTLS and RBAC, and end-to-end observability with industry-standard monitoring tools. The text concludes with forward-looking insights on extensibility, integrating CI/CD MLOps workflows, developing custom components, and navigating multi-cluster or edge deployments. Clear, practical, and up-to-date, this resource empowers ML engineers, architects, and platform teams to deliver resilient, scalable, and explainable ML services at enterprise scale.
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