SageMaker Essentials (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"SageMaker Essentials" "SageMaker Essentials" offers a comprehensive guide to mastering Amazon SageMaker, the leading platform for machine learning at scale. This authoritative resource meticulously explores the platform's architecture, seamlessly guiding readers through elastic infrastructure management, secure data integration, CI/CD pipeline integration, and best practices for leveraging SageMaker Studio and modern SDKs. Emphasizing enterprise needs, the book provides strategies for cost optimization, robust access management, and sustainable machine learning solutions, making it indispensa...
"SageMaker Essentials"
"SageMaker Essentials" offers a comprehensive guide to mastering Amazon SageMaker, the leading platform for machine learning at scale. This authoritative resource meticulously explores the platform's architecture, seamlessly guiding readers through elastic infrastructure management, secure data integration, CI/CD pipeline integration, and best practices for leveraging SageMaker Studio and modern SDKs. Emphasizing enterprise needs, the book provides strategies for cost optimization, robust access management, and sustainable machine learning solutions, making it indispensable for organizations seeking operational efficiency in cloud-based AI deployments.
With a keen focus on advanced data preparation, readers learn how to automate data wrangling, engineer reusable transformation pipelines, and proactively monitor data quality and drift. The book also delves into complex model training scenarios, such as distributed and multi-node training, hyperparameter optimization, and interactive experimentation, all while maintaining strict budgeting and resource usage control. The end-to-end lifecycle of machine learning, from data processing and labeling with Ground Truth to robust deployment strategies-including real-time, batch, and serverless inference-is covered with practical patterns and production-targeted guidance.
Equipped for the demands of modern MLOps, "SageMaker Essentials" details the automation of ML pipelines, advanced monitoring and observability with CloudWatch, and compliance-driven security, governance, and auditability frameworks. Readers will benefit from chapters on hybrid architectures, event-driven workflows, federated learning, and extensibility with open-source and SaaS integrations. Detailed coverage of incident detection, automated remediation, and cost and environmental considerations round out this essential reference for data scientists, ML engineers, architects, and technology leaders committed to scaling secure, compliant, and efficient AI systems on AWS.
"SageMaker Essentials" offers a comprehensive guide to mastering Amazon SageMaker, the leading platform for machine learning at scale. This authoritative resource meticulously explores the platform's architecture, seamlessly guiding readers through elastic infrastructure management, secure data integration, CI/CD pipeline integration, and best practices for leveraging SageMaker Studio and modern SDKs. Emphasizing enterprise needs, the book provides strategies for cost optimization, robust access management, and sustainable machine learning solutions, making it indispensable for organizations seeking operational efficiency in cloud-based AI deployments.
With a keen focus on advanced data preparation, readers learn how to automate data wrangling, engineer reusable transformation pipelines, and proactively monitor data quality and drift. The book also delves into complex model training scenarios, such as distributed and multi-node training, hyperparameter optimization, and interactive experimentation, all while maintaining strict budgeting and resource usage control. The end-to-end lifecycle of machine learning, from data processing and labeling with Ground Truth to robust deployment strategies-including real-time, batch, and serverless inference-is covered with practical patterns and production-targeted guidance.
Equipped for the demands of modern MLOps, "SageMaker Essentials" details the automation of ML pipelines, advanced monitoring and observability with CloudWatch, and compliance-driven security, governance, and auditability frameworks. Readers will benefit from chapters on hybrid architectures, event-driven workflows, federated learning, and extensibility with open-source and SaaS integrations. Detailed coverage of incident detection, automated remediation, and cost and environmental considerations round out this essential reference for data scientists, ML engineers, architects, and technology leaders committed to scaling secure, compliant, and efficient AI systems on AWS.
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