Envío gratis siempre a todo México.
Haz clic para hacer zoom
Not Available
William Smith
Pachyderm Workflows for Machine Learning
Cargando comentarios…
Comparte
$186
Envío gratis siempre
Comentarios
Cargando el resumen…
Cargando comentarios…
product
7660448Pachyderm Workflows for Machine Learninghttps://www.gandhi.com.mx/pachyderm-workflows-for-machine-learning-6610000973903/phttps://gandhi.vtexassets.com/arquivos/ids/7287732/image.jpg?v=638889880860430000186186MXNGandhiInStock/Ebooks/<p>"Pachyderm Workflows for Machine Learning"</p><p>"Pachyderm Workflows for Machine Learning" is a definitive guide to mastering data-centric pipelines and reproducible workflow orchestration using Pachyderm. The book systematically unpacks the platforms foundational architecture, from its innovative data versioning and provenance models to the practical interplay with Kubernetes and container technologies. Readers are equipped with a deep technical understanding of system scaling, resiliency, and storage models critical for robust machine learning operations across on-premises, cloud, and hybrid infrastructures.</p><p>Delving into the intricacies of pipeline design, the book navigates through declarative specifications, multi-stage data transformations, and seamless integration with leading machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn. Emphasis is placed on building resilient, automated, and reusable MLOps pipelines, alongside advanced strategies for resource optimization, governance, and collaborative artifact management. Real-world practices for system monitoring, upgrades, and disaster recovery are paired with expert insights on security, compliance, and policy enforcement for regulated environments.</p><p>With dedicated chapters on performance engineering, hyperparameter search, active learning, and productionizing research pipelines, this resource bridges the gap between ML science and scalable engineering. Readers will discover proven blueprints for automating end-to-end workflows, ensuring data integrity, and extending Pachyderms capabilities within the broader machine learning ecosystem. Whether you are an ML engineer, data scientist, or platform architect, this book provides actionable methodologies and forward-looking guidance to empower sustainable, traceable, and high-performance machine learning operations.</p>...7267989Pachyderm Workflows for Machine Learning186186https://www.gandhi.com.mx/pachyderm-workflows-for-machine-learning-6610000973903/phttps://gandhi.vtexassets.com/arquivos/ids/7287732/image.jpg?v=638889880860430000InStockMXN99999DIEbook20256610000973903_W3siaWQiOiIzMGNlYzEzNC01ZmEzLTQ0ZWYtOWQzOS1iYjMwYzk2ZjI2NWQiLCJsaXN0UHJpY2UiOjE4NiwiZGlzY291bnQiOjAsInNlbGxpbmdQcmljZSI6MTg2LCJpbmNsdWRlc1RheCI6dHJ1ZSwicHJpY2VUeXBlIjoiSXBwIiwiY3VycmVuY3kiOiJNWE4iLCJmcm9tIjoiMjAyNS0wNy0yNFQxOTowMDowMFoiLCJyZWdpb24iOiJNWCIsImlzUHJlb3JkZXIiOmZhbHNlfV0=6610000973903_<p>"Pachyderm Workflows for Machine Learning"</p><p>"Pachyderm Workflows for Machine Learning" is a definitive guide to mastering data-centric pipelines and reproducible workflow orchestration using Pachyderm. The book systematically unpacks the platforms foundational architecture, from its innovative data versioning and provenance models to the practical interplay with Kubernetes and container technologies. Readers are equipped with a deep technical understanding of system scaling, resiliency, and storage models critical for robust machine learning operations across on-premises, cloud, and hybrid infrastructures.</p><p>Delving into the intricacies of pipeline design, the book navigates through declarative specifications, multi-stage data transformations, and seamless integration with leading machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn. Emphasis is placed on building resilient, automated, and reusable MLOps pipelines, alongside advanced strategies for resource optimization, governance, and collaborative artifact management. Real-world practices for system monitoring, upgrades, and disaster recovery are paired with expert insights on security, compliance, and policy enforcement for regulated environments.</p><p>With dedicated chapters on performance engineering, hyperparameter search, active learning, and productionizing research pipelines, this resource bridges the gap between ML science and scalable engineering. Readers will discover proven blueprints for automating end-to-end workflows, ensuring data integrity, and extending Pachyderms capabilities within the broader machine learning ecosystem. Whether you are an ML engineer, data scientist, or platform architect, this book provides actionable methodologies and forward-looking guidance to empower sustainable, traceable, and high-performance machine learning operations.</p>...6610000973903_HiTeX Presslibro_electonico_6610000973903_6610000973903William SmithInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/content2connect_drm-epub-07509fe6-b3ec-46b2-9161-30950e4e34ba.epub2025-07-24T00:00:00+00:00HiTeX Press