MosaicML Inference Architecture and Deployment (eBook, ePUB)
The Complete Guide for Developers and Engineers
PAYBACK Punkte
0 °P sammeln!
"MosaicML Inference Architecture and Deployment" "MosaicML Inference Architecture and Deployment" presents a comprehensive exploration of state-of-the-art solutions for scalable, secure, and efficient machine learning inference. The book opens with a deep dive into the foundations of inference, charting MosaicML's evolution, its core guiding philosophies, and the nuanced distinctions between training and serving paradigms. Through a thoughtful examination of architectural principles and serving taxonomies, readers will gain insight into modern model-serving challenges, stakeholder needs, and r...
"MosaicML Inference Architecture and Deployment"
"MosaicML Inference Architecture and Deployment" presents a comprehensive exploration of state-of-the-art solutions for scalable, secure, and efficient machine learning inference. The book opens with a deep dive into the foundations of inference, charting MosaicML's evolution, its core guiding philosophies, and the nuanced distinctions between training and serving paradigms. Through a thoughtful examination of architectural principles and serving taxonomies, readers will gain insight into modern model-serving challenges, stakeholder needs, and robust requirements engineering for diverse machine learning workloads.
Spanning detailed technical depths, the book systematically unpacks the core components underpinning a modern inference system, from the intricacies of server threading and resource management to advanced model management, data pipelines, and request-handling protocols. It covers end-to-end deployment and automation practices-including CI/CD, containerization, release engineering, and reproducible workflows-while addressing advanced rollout strategies, validation, and continuous monitoring techniques. Special emphasis is placed on scalability: from load balancing and high availability to multi-model, multi-tenant environments, and integration with cloud and hybrid infrastructures.
With dedicated chapters on hardware acceleration, optimization, security, and observability, "MosaicML Inference Architecture and Deployment" offers pragmatic guidance for deploying and operating inference pipelines at scale. Topics such as GPU/TPU integration, model compression, energy efficiency, compliance, and privacy-preserving inference are treated with equal rigor. The book concludes by exploring emerging trends, including federated and edge inference, AutoML-driven operations, zero trust architectures, and the scaling of large model serving, making it an indispensable reference for engineers, architects, and researchers building robust machine learning infrastructure.
"MosaicML Inference Architecture and Deployment" presents a comprehensive exploration of state-of-the-art solutions for scalable, secure, and efficient machine learning inference. The book opens with a deep dive into the foundations of inference, charting MosaicML's evolution, its core guiding philosophies, and the nuanced distinctions between training and serving paradigms. Through a thoughtful examination of architectural principles and serving taxonomies, readers will gain insight into modern model-serving challenges, stakeholder needs, and robust requirements engineering for diverse machine learning workloads.
Spanning detailed technical depths, the book systematically unpacks the core components underpinning a modern inference system, from the intricacies of server threading and resource management to advanced model management, data pipelines, and request-handling protocols. It covers end-to-end deployment and automation practices-including CI/CD, containerization, release engineering, and reproducible workflows-while addressing advanced rollout strategies, validation, and continuous monitoring techniques. Special emphasis is placed on scalability: from load balancing and high availability to multi-model, multi-tenant environments, and integration with cloud and hybrid infrastructures.
With dedicated chapters on hardware acceleration, optimization, security, and observability, "MosaicML Inference Architecture and Deployment" offers pragmatic guidance for deploying and operating inference pipelines at scale. Topics such as GPU/TPU integration, model compression, energy efficiency, compliance, and privacy-preserving inference are treated with equal rigor. The book concludes by exploring emerging trends, including federated and edge inference, AutoML-driven operations, zero trust architectures, and the scaling of large model serving, making it an indispensable reference for engineers, architects, and researchers building robust machine learning infrastructure.
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
