Kedro for Data Engineering and Machine Learning Pipelines (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Kedro for Data Engineering and Machine Learning Pipelines" "Kedro for Data Engineering and Machine Learning Pipelines" is a comprehensive guide that expertly navigates the intricacies of building robust, production-grade data and machine learning solutions using Kedro. The book begins by introducing the foundational architecture and principles behind Kedro, emphasizing modularity, maintainability, and reproducibility. Through detailed exploration of project structure, configuration patterns, and customizable extensions, readers develop a deep understanding of the framework's core components a...
"Kedro for Data Engineering and Machine Learning Pipelines" "Kedro for Data Engineering and Machine Learning Pipelines" is a comprehensive guide that expertly navigates the intricacies of building robust, production-grade data and machine learning solutions using Kedro. The book begins by introducing the foundational architecture and principles behind Kedro, emphasizing modularity, maintainability, and reproducibility. Through detailed exploration of project structure, configuration patterns, and customizable extensions, readers develop a deep understanding of the framework's core components and the best practices that underpin successful Kedro projects. Structured across multiple advanced topics, the book delves into the complexities of orchestrating scalable data engineering workflows and constructing modular, traceable machine learning pipelines. Readers will discover hands-on strategies for ETL pipeline orchestration, data ingestion from diverse sources, transformation at scale, and real-time incremental loading. For machine learning professionals, dedicated chapters highlight best practices in feature engineering, model experimentation, hyperparameter optimization, and seamless pipeline integration with model serving and monitoring-ensuring that workflows are not only effective, but also reproducible and scalable. Recognizing the importance of operational excellence and real-world application, this volume covers essential topics such as workflow orchestration with external schedulers, automated CI/CD deployment, pipeline observability, and robust testing frameworks. Further chapters address security, governance, and compliance, while also showcasing advanced customization and integration patterns to support cloud-native and enterprise environments. Augmented with practical case studies and actionable insights, "Kedro for Data Engineering and Machine Learning Pipelines" empowers data engineers, ML practitioners, and platform architects to confidently deliver high-quality, maintainable, and scalable data solutions with Kedro.
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