Building Production Machine Learning Pipelines with AWS SageMaker: The Complete Guide for Developers and Engineers
William Smith
Jul 2025 · HiTeX Press
Ebook
250
Pages
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About this ebook
"Building Production Machine Learning Pipelines with AWS SageMaker"
"Building Production Machine Learning Pipelines with AWS SageMaker" is a comprehensive guide for engineers, architects, and data scientists who aspire to deliver robust, scalable, and secure machine learning solutions in production environments. This book meticulously details every phase of the machine learning lifecycle on AWS, beginning with foundational principles of cloud-native system design, exploring the intricacies of the SageMaker platform in the broader AWS ecosystem, and addressing the operational, security, and compliance demands of real-world production pipelines.
The book delves into advanced data management strategies, including architecting efficient data lakes, automating high-quality data ingestion and labeling, managing sensitive and regulated data, and leveraging SageMaker's feature engineering capabilities for optimal reuse and collaboration. Readers will gain practical knowledge in designing reproducible experimentation workflows, implementing custom model training using containers, orchestrating large-scale distributed training jobs, and managing technical debt throughout continuous development.
With in-depth coverage of MLOps automation, the text covers declarative infrastructure, robust CI/CD practices, and advanced testing paradigms, ensuring dependable and auditable operations. Security and compliance are woven throughout, offering insights into IAM, network isolation, encryption, and incident response. The book rounds out with practical guidance on deployment patterns, live model monitoring, cost optimization, and future-ready architectures involving federated learning, real-time streaming, and open-source integrations. Whether transitioning to production or optimizing mature ML workflows, this book equips professionals with the architectural blueprints and operational best practices to excel at scale with AWS SageMaker.
"Building Production Machine Learning Pipelines with AWS SageMaker" is a comprehensive guide for engineers, architects, and data scientists who aspire to deliver robust, scalable, and secure machine learning solutions in production environments. This book meticulously details every phase of the machine learning lifecycle on AWS, beginning with foundational principles of cloud-native system design, exploring the intricacies of the SageMaker platform in the broader AWS ecosystem, and addressing the operational, security, and compliance demands of real-world production pipelines.
The book delves into advanced data management strategies, including architecting efficient data lakes, automating high-quality data ingestion and labeling, managing sensitive and regulated data, and leveraging SageMaker's feature engineering capabilities for optimal reuse and collaboration. Readers will gain practical knowledge in designing reproducible experimentation workflows, implementing custom model training using containers, orchestrating large-scale distributed training jobs, and managing technical debt throughout continuous development.
With in-depth coverage of MLOps automation, the text covers declarative infrastructure, robust CI/CD practices, and advanced testing paradigms, ensuring dependable and auditable operations. Security and compliance are woven throughout, offering insights into IAM, network isolation, encryption, and incident response. The book rounds out with practical guidance on deployment patterns, live model monitoring, cost optimization, and future-ready architectures involving federated learning, real-time streaming, and open-source integrations. Whether transitioning to production or optimizing mature ML workflows, this book equips professionals with the architectural blueprints and operational best practices to excel at scale with AWS SageMaker.
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