Practical RapidMiner Workflows and Automation (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Practical RapidMiner Workflows and Automation" "Practical RapidMiner Workflows and Automation" is a comprehensive guide that elevates your data science and machine learning practice with expert strategies for workflow development, automation, and operationalization. The book begins with a meticulous examination of the RapidMiner ecosystem-unpacking the architecture, integration points, extension frameworks, and security dimensions essential for deploying RapidMiner in modern enterprise environments. Readers gain a foundational understanding of deployment strategies, from on-premises to hybrid...
"Practical RapidMiner Workflows and Automation"
"Practical RapidMiner Workflows and Automation" is a comprehensive guide that elevates your data science and machine learning practice with expert strategies for workflow development, automation, and operationalization. The book begins with a meticulous examination of the RapidMiner ecosystem-unpacking the architecture, integration points, extension frameworks, and security dimensions essential for deploying RapidMiner in modern enterprise environments. Readers gain a foundational understanding of deployment strategies, from on-premises to hybrid cloud settings, and best practices for authenticating and securing automated workflows.
Progressing through advanced workflow design patterns, the book delves into the nuances of modular, reusable workflows, robust parameterization, and orchestrating complex, error-resilient pipelines. Complete with practical guidance on data acquisition automation, scalable data cleaning, and evolving schema management, the text equips practitioners to build repeatable and traceable data processes. Readers will also master the automation of sophisticated machine learning pipelines, including automated model selection, hyperparameter tuning, and workflow-driven validation, with coverage of ensemble methods and temporal data modeling.
The latter chapters empower organizations to operationalize and monitor production systems at scale, featuring workflow automation for deployment, versioning, CI/CD, and ML model monitoring. Best-in-class approaches for governance, audit trails, compliance (e.g., GDPR/CCPA), and explainability are detailed, ensuring enterprise readiness. Augmented by advanced case studies spanning finance, healthcare, manufacturing, and retail, this book offers a blend of technical rigor and real-world insight-making it an indispensable resource for data professionals seeking to unlock the full automation potential of RapidMiner.
"Practical RapidMiner Workflows and Automation" is a comprehensive guide that elevates your data science and machine learning practice with expert strategies for workflow development, automation, and operationalization. The book begins with a meticulous examination of the RapidMiner ecosystem-unpacking the architecture, integration points, extension frameworks, and security dimensions essential for deploying RapidMiner in modern enterprise environments. Readers gain a foundational understanding of deployment strategies, from on-premises to hybrid cloud settings, and best practices for authenticating and securing automated workflows.
Progressing through advanced workflow design patterns, the book delves into the nuances of modular, reusable workflows, robust parameterization, and orchestrating complex, error-resilient pipelines. Complete with practical guidance on data acquisition automation, scalable data cleaning, and evolving schema management, the text equips practitioners to build repeatable and traceable data processes. Readers will also master the automation of sophisticated machine learning pipelines, including automated model selection, hyperparameter tuning, and workflow-driven validation, with coverage of ensemble methods and temporal data modeling.
The latter chapters empower organizations to operationalize and monitor production systems at scale, featuring workflow automation for deployment, versioning, CI/CD, and ML model monitoring. Best-in-class approaches for governance, audit trails, compliance (e.g., GDPR/CCPA), and explainability are detailed, ensuring enterprise readiness. Augmented by advanced case studies spanning finance, healthcare, manufacturing, and retail, this book offers a blend of technical rigor and real-world insight-making it an indispensable resource for data professionals seeking to unlock the full automation potential of RapidMiner.
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