SageMaker Deployment and Development: Definitive Reference for Developers and Engineers
Richard Johnson
jun. 2025 · HiTeX Press
E-bog
250
Sider
family_home
Kvalificeret
info
reportBedømmelser og anmeldelser verificeres ikke Få flere oplysninger
Om denne e-bog
"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.
Bedøm denne e-bog
Fortæl os, hvad du mener.
Oplysninger om læsning
Smartphones og tablets
Installer appen Google Play Bøger til Android og iPad/iPhone. Den synkroniserer automatisk med din konto og giver dig mulighed for at læse online eller offline, uanset hvor du er.
Bærbare og stationære computere
Du kan høre lydbøger, du har købt i Google Play via browseren på din computer.
e-læsere og andre enheder
Hvis du vil læse på e-ink-enheder som f.eks. Kobo-e-læsere, skal du downloade en fil og overføre den til din enhed. Følg den detaljerede vejledning i Hjælp for at overføre filerne til understøttede e-læsere.
