SageMaker Deployment and Development: Definitive Reference for Developers and Engineers
Richard Johnson
јун 2025. · HiTeX Press
Е-књига
250
Страница
family_home
Испуњава услове
info
reportОцене и рецензије нису верификоване Сазнајте више
О овој е-књизи
"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.
Оцените ову е-књигу
Јавите нам своје мишљење.
Информације о читању
Паметни телефони и таблети
Инсталирајте апликацију Google Play књиге за Android и iPad/iPhone. Аутоматски се синхронизује са налогом и омогућава вам да читате онлајн и офлајн где год да се налазите.
Лаптопови и рачунари
Можете да слушате аудио-књиге купљене на Google Play-у помоћу веб-прегледача на рачунару.
Е-читачи и други уређаји
Да бисте читали на уређајима које користе е-мастило, као што су Kobo е-читачи, треба да преузмете фајл и пренесете га на уређај. Пратите детаљна упутства из центра за помоћ да бисте пренели фајлове у подржане е-читаче.
