SageMaker Deployment and Development: Definitive Reference for Developers and Engineers
Richard Johnson
qer 2025 · HiTeX Press
Libër elektronik
250
Faqe
family_home
I përshtatshëm
info
reportVlerësimet dhe komentet nuk janë të verifikuara Mëso më shumë
Rreth këtij libri elektronik
"SageMaker Deployment and Development"
"SageMaker Deployment and Development" is an authoritative guide to mastering the full spectrum of machine learning (ML) workflows using AWS SageMaker. This comprehensive book dives deep into SageMaker’s modular architecture, unraveling the intricacies of its core components such as Studio, Training, Inference, Processing, and Feature Store. Readers acquire actionable insights into managing containerized environments, integrating with the broader AWS ecosystem, and architecting data flows for scalability, security, and efficiency. Advanced discussions explore distributed computing strategies, cost optimization, and high-performance resource management—enabling ML professionals to build robust, enterprise-grade deployments.
The volume thoroughly addresses advanced model development workflows, guiding practitioners from experiment tracking and custom algorithm containers to hyperparameter optimization and versioned feature engineering. Readers will discover best practices for reproducibility, environment management, and multi-framework integration with leading ML libraries such as PyTorch, TensorFlow, and Scikit-learn. Rich coverage of data engineering tackles automated pipelines, batch and streaming data integration, and seamless connections to data lakes and warehouses, all underpinned by stringent quality, validation, and auditability principles.
Recognizing the demands of operating ML in production, the book dedicates extensive chapters to security, compliance, and governance, offering practical solutions for regulated industries and multi-tenant environments. It surveys the state of MLOps with hands-on techniques for CI/CD, automated testing, and controlled model promotion. Techniques for large-scale, distributed training, inference endpoint management, monitoring, and drift detection are paired with insights into extensibility, custom integrations, and future trends. Whether you’re a data scientist, ML engineer, or cloud architect, "SageMaker Deployment and Development" equips you with the knowledge and skills to deliver secure, scalable, and future-proof ML solutions on AWS.
"SageMaker Deployment and Development" is an authoritative guide to mastering the full spectrum of machine learning (ML) workflows using AWS SageMaker. This comprehensive book dives deep into SageMaker’s modular architecture, unraveling the intricacies of its core components such as Studio, Training, Inference, Processing, and Feature Store. Readers acquire actionable insights into managing containerized environments, integrating with the broader AWS ecosystem, and architecting data flows for scalability, security, and efficiency. Advanced discussions explore distributed computing strategies, cost optimization, and high-performance resource management—enabling ML professionals to build robust, enterprise-grade deployments.
The volume thoroughly addresses advanced model development workflows, guiding practitioners from experiment tracking and custom algorithm containers to hyperparameter optimization and versioned feature engineering. Readers will discover best practices for reproducibility, environment management, and multi-framework integration with leading ML libraries such as PyTorch, TensorFlow, and Scikit-learn. Rich coverage of data engineering tackles automated pipelines, batch and streaming data integration, and seamless connections to data lakes and warehouses, all underpinned by stringent quality, validation, and auditability principles.
Recognizing the demands of operating ML in production, the book dedicates extensive chapters to security, compliance, and governance, offering practical solutions for regulated industries and multi-tenant environments. It surveys the state of MLOps with hands-on techniques for CI/CD, automated testing, and controlled model promotion. Techniques for large-scale, distributed training, inference endpoint management, monitoring, and drift detection are paired with insights into extensibility, custom integrations, and future trends. Whether you’re a data scientist, ML engineer, or cloud architect, "SageMaker Deployment and Development" equips you with the knowledge and skills to deliver secure, scalable, and future-proof ML solutions on AWS.
Vlerëso këtë libër elektronik
Na trego se çfarë mendon.
Informacione për leximin
Telefona inteligjentë dhe tabletë
Instalo aplikacionin "Librat e Google Play" për Android dhe iPad/iPhone. Ai sinkronizohet automatikisht me llogarinë tënde dhe të lejon të lexosh online dhe offline kudo që të ndodhesh.
Laptopë dhe kompjuterë
Mund të dëgjosh librat me audio të blerë në Google Play duke përdorur shfletuesin e uebit të kompjuterit.
Lexuesit elektronikë dhe pajisjet e tjera
Për të lexuar në pajisjet me bojë elektronike si p.sh. lexuesit e librave elektronikë Kobo, do të të duhet të shkarkosh një skedar dhe ta transferosh atë te pajisja jote. Ndiq udhëzimet e detajuara në Qendrën e ndihmës për të transferuar skedarët te lexuesit e mbështetur të librave elektronikë.
