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RapidMiner Workflow Engineering: Automate, Optimize, and Scale Data Science Pipelines is a practical, enterprise-focused guide that transforms how teams design, deploy, and maintain RapidMiner solutions. Beginning with a clear exposition of the RapidMiner ecosystem, the book unpacks core architecture, integration points, extension frameworks, and security considerations, and it presents deployment strategies for on-premises, cloud, and hybrid environments. Readers will learn best practices for authentication, access control, and secure orchestration of automated workflows to meet modern operational and compliance demands.The heart of the book teaches robust workflow engineering: creating modular, reusable pipelines, systematic parameterization, and patterns for error-resilient orchestration. You'll master automated data acquisition, scalable data cleaning, evolving schema management, and the construction of sophisticated machine learning pipelines-including automated model selection, hyperparameter optimization, ensembles, and temporal modeling-while ensuring reproducibility, traceability, and rigorous validation throughout the lifecycle.Finally, the text guides practitioners through operationalization at scale with practical techniques for CI/CD, versioning, deployment automation, and production monitoring of models and workflows. Governance, audit trails, explainability, and compliance (GDPR/CCPA) are integrated into each stage to support enterprise readiness. Illustrated with advanced case studies across finance, healthcare, manufacturing, and retail, this book equips data professionals and engineering teams to reliably automate, optimize, and scale their RapidMiner-driven data science initiatives.
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