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The MLOps Cycle: Automate Your Machine Learning Workflow from Data Pipelines to Model Monitoring with Kubeflow, Airflow, and Prometheus - Couverture souple
Synopsis
Transform your machine learning projects into production-grade systems with The MLOps Cycle, your hands-on guide to mastering the complete ML lifecycle—from raw data ingestion to automated monitoring at scale.
Designed for data scientists, ML engineers, and DevOps practitioners, this book dives deep into the real-world tools and practices that power modern AI deployment. Learn how to streamline and automate every stage of your ML workflow using industry-standard technologies like Kubeflow, Apache Airflow, and Prometheus.
Through practical examples and project-driven chapters, you’ll:
Design robust data pipelines that handle preprocessing, validation, and feature engineering.
Orchestrate end-to-end ML workflows with Airflow DAGs and Kubeflow Pipelines.
Package, deploy, and version your models in cloud-native environments.
Implement CI/CD pipelines tailored for ML with reproducibility and scalability in mind.
Monitor model drift, performance, and system health using Prometheus and Grafana.
Align machine learning operations with MLOps best practices across teams and lifecycles.
Whether you’re building your first ML system or scaling enterprise-grade models in production, The MLOps Cycle offers the clarity, architecture, and automation mindset needed to succeed.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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The MLOps Cycle: Automate Your Machine Learning Workflow from Data Pipelines to Model Monitoring with Kubeflow, Airflow, and Prometheus
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The MLOps Cycle (Paperback)
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Paperback. Etat : new. Paperback. Transform your machine learning projects into production-grade systems with The MLOps Cycle, your hands-on guide to mastering the complete ML lifecycle-from raw data ingestion to automated monitoring at scale.Designed for data scientists, ML engineers, and DevOps practitioners, this book dives deep into the real-world tools and practices that power modern AI deployment. Learn how to streamline and automate every stage of your ML workflow using industry-standard technologies like Kubeflow, Apache Airflow, and Prometheus.Through practical examples and project-driven chapters, you'll: Design robust data pipelines that handle preprocessing, validation, and feature engineering.Orchestrate end-to-end ML workflows with Airflow DAGs and Kubeflow Pipelines.Package, deploy, and version your models in cloud-native environments.Implement CI/CD pipelines tailored for ML with reproducibility and scalability in mind.Monitor model drift, performance, and system health using Prometheus and Grafana.Align machine learning operations with MLOps best practices across teams and lifecycles.Whether you're building your first ML system or scaling enterprise-grade models in production, The MLOps Cycle offers the clarity, architecture, and automation mindset needed to succeed. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798294156671
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The MLOps Cycle
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The MLOps Cycle: Automate Your Machine Learning Workflow from Data Pipelines to Model Monitoring with Kubeflow, Airflow, and Prometheus
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The MLOps Cycle: Automate Your Machine Learning Workflow from Data Pipelines to Model Monitoring with Kubeflow, Airflow, and Prometheus
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Independently published, 2025
ISBN 13 : 9798294156671
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The MLOps Cycle
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798294156671
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The MLOps Cycle: Automate Your Machine Learning Workflow from Data Pipelines to Model Monitoring with Kubeflow, Airflow, and Prometheus
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The MLOps Cycle: Automate Your Machine Learning Workflow from Data Pipelines to Model Monitoring with Kubeflow, Airflow, and Prometheus
Edité par
Independently published, 2025
ISBN 13 : 9798294156671
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 51310851
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Expédition depuis Royaume-Uni vers Etats-Unis
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The MLOps Cycle (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Transform your machine learning projects into production-grade systems with The MLOps Cycle, your hands-on guide to mastering the complete ML lifecycle-from raw data ingestion to automated monitoring at scale.Designed for data scientists, ML engineers, and DevOps practitioners, this book dives deep into the real-world tools and practices that power modern AI deployment. Learn how to streamline and automate every stage of your ML workflow using industry-standard technologies like Kubeflow, Apache Airflow, and Prometheus.Through practical examples and project-driven chapters, you'll: Design robust data pipelines that handle preprocessing, validation, and feature engineering.Orchestrate end-to-end ML workflows with Airflow DAGs and Kubeflow Pipelines.Package, deploy, and version your models in cloud-native environments.Implement CI/CD pipelines tailored for ML with reproducibility and scalability in mind.Monitor model drift, performance, and system health using Prometheus and Grafana.Align machine learning operations with MLOps best practices across teams and lifecycles.Whether you're building your first ML system or scaling enterprise-grade models in production, The MLOps Cycle offers the clarity, architecture, and automation mindset needed to succeed. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798294156671
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Chesterfield, G: MLOps Cycle
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Taschenbuch. Etat : Neu. Neuware - Transform your machine learning projects into production-grade systems with The MLOps Cycle, your hands-on guide to mastering the complete ML lifecycle-from raw data ingestion to automated monitoring at scale. N° de réf. du vendeur 9798294156671
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