Applied Machine Learning with MLlib (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Applied Machine Learning with MLlib" Harness the full potential of large-scale machine learning with "Applied Machine Learning with MLlib," a comprehensive guide designed for practitioners and engineers working in modern data environments. This book delves into the architectural pillars of Apache Spark and MLlib, illuminating the principles of distributed computing that enable robust, scalable machine learning solutions in production. Readers will gain a deep understanding of core internals, from resilient distributed datasets and resource management to API evolution and fault-tolerant deploy...
"Applied Machine Learning with MLlib"
Harness the full potential of large-scale machine learning with "Applied Machine Learning with MLlib," a comprehensive guide designed for practitioners and engineers working in modern data environments. This book delves into the architectural pillars of Apache Spark and MLlib, illuminating the principles of distributed computing that enable robust, scalable machine learning solutions in production. Readers will gain a deep understanding of core internals, from resilient distributed datasets and resource management to API evolution and fault-tolerant deployment strategies-empowering them to architect high-performance ML systems across clusters and clouds.
Covering the entire machine learning pipeline, the book offers practical guidance on data ingestion, transformation, feature engineering, and both supervised and unsupervised algorithm implementation at scale. In-depth walkthroughs demonstrate best practices for model evaluation, hyperparameter optimization, clustering, and anomaly detection-all tailored for the realities of distributed data. With dedicated chapters on automation, reproducibility, and model management, readers will learn to design robust ML pipelines, custom transformers, and orchestrate reproducible experiments using industry-standard tools.
Beyond foundational topics, the book explores advanced capabilities including streaming analytics, online learning, federated privacy-preserving ML, graph-based approaches, and distributed deep learning integrations. Real-world case studies in personalization, NLP, predictive maintenance, fraud detection, and healthcare illustrate end-to-end solutions and organizational best practices. Whether deploying at web scale or tackling sensitive data environments, "Applied Machine Learning with MLlib" equips professionals with practical patterns and expert insights for building, optimizing, and maintaining state-of-the-art ML applications using Spark's powerful ecosystem.
Harness the full potential of large-scale machine learning with "Applied Machine Learning with MLlib," a comprehensive guide designed for practitioners and engineers working in modern data environments. This book delves into the architectural pillars of Apache Spark and MLlib, illuminating the principles of distributed computing that enable robust, scalable machine learning solutions in production. Readers will gain a deep understanding of core internals, from resilient distributed datasets and resource management to API evolution and fault-tolerant deployment strategies-empowering them to architect high-performance ML systems across clusters and clouds.
Covering the entire machine learning pipeline, the book offers practical guidance on data ingestion, transformation, feature engineering, and both supervised and unsupervised algorithm implementation at scale. In-depth walkthroughs demonstrate best practices for model evaluation, hyperparameter optimization, clustering, and anomaly detection-all tailored for the realities of distributed data. With dedicated chapters on automation, reproducibility, and model management, readers will learn to design robust ML pipelines, custom transformers, and orchestrate reproducible experiments using industry-standard tools.
Beyond foundational topics, the book explores advanced capabilities including streaming analytics, online learning, federated privacy-preserving ML, graph-based approaches, and distributed deep learning integrations. Real-world case studies in personalization, NLP, predictive maintenance, fraud detection, and healthcare illustrate end-to-end solutions and organizational best practices. Whether deploying at web scale or tackling sensitive data environments, "Applied Machine Learning with MLlib" equips professionals with practical patterns and expert insights for building, optimizing, and maintaining state-of-the-art ML applications using Spark's powerful ecosystem.
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