Seldon Core Triton Integration for Scalable Model Serving (eBook, ePUB)
The Complete Guide for Developers and Engineers
PAYBACK Punkte
3 °P sammeln!
"Seldon Core Triton Integration for Scalable Model Serving" "Seldon Core Triton Integration for Scalable Model Serving" is a comprehensive guide that delves into the intricacies of deploying, scaling, and managing machine learning models in modern production environments. The book opens by establishing a clear foundation in model serving concepts, addressing both the objectives and challenges that organizations face as they push AI workloads into robust, scalable pipelines. Through careful exploration of architectural paradigms—ranging from monolithic to microservices and serverless patterns...
"Seldon Core Triton Integration for Scalable Model Serving"
"Seldon Core Triton Integration for Scalable Model Serving" is a comprehensive guide that delves into the intricacies of deploying, scaling, and managing machine learning models in modern production environments. The book opens by establishing a clear foundation in model serving concepts, addressing both the objectives and challenges that organizations face as they push AI workloads into robust, scalable pipelines. Through careful exploration of architectural paradigms—ranging from monolithic to microservices and serverless patterns—the author explicates how cloud-native technologies such as Kubernetes and containerization empower teams to deliver resilient and portable AI solutions. Foundational metrics like latency, throughput, and reliability are meticulously covered, equipping readers with the knowledge needed to evaluate serving performance at scale.
The heart of the book provides a dual deep dive into the architectures of Seldon Core and NVIDIA Triton Inference Server. Readers are guided through the configuration and orchestration of inference pipelines, from leveraging Seldon’s custom resources and inference graphs to harnessing Triton’s advanced hardware utilization features, support for multiple frameworks, and extension capabilities via custom backends and ensembles. Detailed chapters highlight the integration patterns between Seldon and Triton—demonstrating practical techniques to wrap Triton endpoints with Seldon predictive units, integrate telemetry, and maintain robust security postures. Best practices for multi-tenancy, workload isolation, observability, and high availability ensure that practitioners can confidently design and operate production-grade, multi-model serving infrastructures.
The book distinguishes itself with its focus on operational excellence and forward-looking strategies. Advanced sections cover resource management, autoscaling, CI/CD workflows, and incident response for serving infrastructure. The coverage of security and compliance—including zero trust networking, RBAC, secrets management, and privacy regulations—offers a pragmatic blueprint for safeguarding real-world deployments. Finally, the narrative extends to emerging trends such as federated serving, serverless inference, adaptive resource management, and integration with feature stores, rounding out a must-have reference for both AI/ML engineers and platform architects seeking to master scalable, cloud-native machine learning deployments.
"Seldon Core Triton Integration for Scalable Model Serving" is a comprehensive guide that delves into the intricacies of deploying, scaling, and managing machine learning models in modern production environments. The book opens by establishing a clear foundation in model serving concepts, addressing both the objectives and challenges that organizations face as they push AI workloads into robust, scalable pipelines. Through careful exploration of architectural paradigms—ranging from monolithic to microservices and serverless patterns—the author explicates how cloud-native technologies such as Kubernetes and containerization empower teams to deliver resilient and portable AI solutions. Foundational metrics like latency, throughput, and reliability are meticulously covered, equipping readers with the knowledge needed to evaluate serving performance at scale.
The heart of the book provides a dual deep dive into the architectures of Seldon Core and NVIDIA Triton Inference Server. Readers are guided through the configuration and orchestration of inference pipelines, from leveraging Seldon’s custom resources and inference graphs to harnessing Triton’s advanced hardware utilization features, support for multiple frameworks, and extension capabilities via custom backends and ensembles. Detailed chapters highlight the integration patterns between Seldon and Triton—demonstrating practical techniques to wrap Triton endpoints with Seldon predictive units, integrate telemetry, and maintain robust security postures. Best practices for multi-tenancy, workload isolation, observability, and high availability ensure that practitioners can confidently design and operate production-grade, multi-model serving infrastructures.
The book distinguishes itself with its focus on operational excellence and forward-looking strategies. Advanced sections cover resource management, autoscaling, CI/CD workflows, and incident response for serving infrastructure. The coverage of security and compliance—including zero trust networking, RBAC, secrets management, and privacy regulations—offers a pragmatic blueprint for safeguarding real-world deployments. Finally, the narrative extends to emerging trends such as federated serving, serverless inference, adaptive resource management, and integration with feature stores, rounding out a must-have reference for both AI/ML engineers and platform architects seeking to master scalable, cloud-native machine learning deployments.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Andere Kunden interessierten sich für
