Kubeflow For Machine Learning
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Kubeflow For Machine Learning
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Author : Trevor Grant
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-13
Kubeflow For Machine Learning written by Trevor Grant and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-13 with Computers categories.
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
Kubeflow For Machine Learning
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Author : L. Trevor Grant
language : en
Publisher:
Release Date : 2021
Kubeflow For Machine Learning written by L. Trevor Grant and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Machine learning categories.
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production.
Kubeflow For Machine Learning
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Author : Holden Karau
language : en
Publisher: O'Reilly Media
Release Date : 2020-12-08
Kubeflow For Machine Learning written by Holden Karau and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with categories.
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Learn how to set up Kubeflow on a cloud provider or on an in-house cluster Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache Spark Learn how to add custom stages such as serving and prediction Keep your model up-to-date with Kubeflow Pipelines Understand how to validate machine learning pipelines
Kubeflow In Action
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Author : Juana Nakfour
language : en
Publisher: Manning
Release Date : 2022-03-29
Kubeflow In Action written by Juana Nakfour and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-29 with Computers categories.
Kubeflow simplifies and automates machine learning tasks like interactive analysis, complex pipelines, and model training. Seamlessly push models to production in the containerized and distributed environment and scale your ML infrastructure from your laptop to a Kubernetes cluster. Kubeflow in Action shows you how to utilize Kubeflow to rapidly scale machine learning projects from a laptop to a distributed cluster. You’ll kick off with a rapid introduction to containers, benefit from careful guidance on Kubeflow’s installation and initial setup, and master core Kubeflow tasks like storing data, training models, and monitoring metrics. Detailed use cases help show how to construct complex pipelines, automate hyperparameter tuning, and implement network architecture search. You’ll quickly progress to a deep dive into Kubeflow’s more advanced uses, including training distributed models, deployment, A/B testing, and infrastructure monitoring to help trigger actions based on incoming data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Kubeflow Operations Guide
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Author : Josh Patterson
language : en
Publisher:
Release Date : 2020
Kubeflow Operations Guide written by Josh Patterson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads-a process Kubeflow makes much easier. With this practical guide, data scientists, data engineers, and platform architects will learn how to plan and execute a Kubeflow project that can support workflows from on-premises to the cloud. Kubeflow is an open source Kubernetes-native platform based on Google's internal machine learning pipelines, and yet major cloud vendors including AWS and Azure advocate the use of Kubernetes and Kubeflow to manage containers and machine learning infrastructure. In today's cloud-based world, this book is ideal for any team planning to build machine learning applications. With this book, you will: Get a concise overview of Kubernetes and Kubeflow Learn how to plan and build a Kubeflow installation Operate, monitor, and automate your installation Provide your Kubeflow installation with adequate security Serve machine learning models on Kubeflow.
Optimizing Machine Learning Pipelines Advanced Techniques With Tensorflow And Kubeflow
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-09
Optimizing Machine Learning Pipelines Advanced Techniques With Tensorflow And Kubeflow written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-09 with Computers categories.
'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is the definitive guide for data scientists, AI practitioners, and technology enthusiasts committed to optimizing their machine learning workflows. This meticulously crafted book offers an in-depth exploration of advanced machine learning operations (MLOps), with a strong focus on the practical deployment, monitoring, and management of machine learning models using TensorFlow and Kubeflow. The journey begins with an overview of machine learning fundamentals and the inner workings of TensorFlow. As readers progress, they delve deeper into data preprocessing, feature engineering, and model building, gradually mastering the complexities of fine-tuning and optimizing models for production readiness. The pivotal aspect of automating machine learning pipelines with Kubeflow is thoroughly examined, empowering readers to deploy TensorFlow models with utmost confidence. Furthermore, the book provides valuable insights into advanced TensorFlow techniques, ethical AI development, and model management with TensorFlow Serving, ensuring comprehensive coverage of key topics. 'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is crafted to elevate its readers into proficient MLOps practitioners, adept at harnessing the power of TensorFlow and Kubeflow to deliver impactful AI solutions. Whether you are embarking on your first machine learning project or seeking to enhance your existing AI capabilities, this book is your essential resource for mastering advanced machine learning operations.
Kubeflow Operations Guide
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Author : Josh Patterson
language : en
Publisher: O'Reilly Media
Release Date : 2020-11-10
Kubeflow Operations Guide written by Josh Patterson and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.
When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. With this practical guide, data scientists, data engineers, and platform architects will learn how to plan and execute a Kubeflow project that can support workflows from on-premises to the cloud. Kubeflow is an open source Kubernetes-native platform based on Google's internal machine learning pipelines, and yet major cloud vendors including AWS and Azure advocate the use of Kubernetes and Kubeflow to manage containers and machine learning infrastructure. In today's cloud-based world, this book is ideal for any team planning to build machine learning applications. With this book, you will: Get a concise overview of Kubernetes and Kubeflow Learn how to plan and build a Kubeflow installation Operate, monitor, and automate your installation Provide your Kubeflow installation with adequate security Serve machine learning models on Kubeflow
Machine Learning In Practice From Pytorch Model To Kubeflow In The Cloud For Bigdata
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Author : Eugeny Shtoltc
language : ru
Publisher: ЛитРес, SelfPub
Release Date : 2020-12-25
Machine Learning In Practice From Pytorch Model To Kubeflow In The Cloud For Bigdata written by Eugeny Shtoltc and has been published by ЛитРес, SelfPub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-25 with Computers categories.
In this book, the Chief Architect of the Department of Architecture and Management of Technical Architecture (Cloud Native Competence Center and the Corporate University of Architects) of Sberbank shares his knowledge and experience with readers in the field of ML. received in the work of the School of Architects and. Author: * guides the reader through the process of creating, learning and developing a neural network, showing in detail with examples * increases horizons, showing how it can take place in BigData from the point of view of the Architect * introduces real models of use in the product environment
The Kubeflow Handbook
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Author : Robert Johnson
language : en
Publisher:
Release Date : 2025
The Kubeflow Handbook written by Robert Johnson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Computers categories.
Machine Learning Operations With Tensorflow And Kubeflow
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Author : Nate Proetean
language : en
Publisher: Independently Published
Release Date : 2024-04-03
Machine Learning Operations With Tensorflow And Kubeflow written by Nate Proetean and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-03 with Computers categories.
"Machine Learning Operations with TensorFlow and Kubeflow" is the essential guide for data scientists, AI practitioners, and anyone looking to streamline their machine learning workflows. This meticulously crafted book offers a comprehensive dive into the world of machine learning operations (MLOps), emphasizing the practical deployment, monitoring, and management of machine learning models. With a strong focus on TensorFlow and Kubeflow, readers will master the art of building robust, scalable, and efficient AI solutions. Starting with the fundamentals of machine learning and the inner workings of TensorFlow, the book progressively unveils the complexities of data preprocessing, feature engineering, and model building. Readers will navigate through the process of fine-tuning and optimizing models, ensuring they are production-ready. The pivotal aspect of automating machine learning pipelines with Kubeflow is thoroughly explored, enabling readers to deploy their TensorFlow models with confidence. Additional insights into advanced TensorFlow techniques, ethical AI development, and model management with TensorFlow Serving ensure this book covers all bases. "Machine Learning Operations with TensorFlow and Kubeflow" is designed to transform its readers into proficient MLOps practitioners, capable of leveraging the power of TensorFlow and Kubeflow to deliver impactful machine learning projects. Whether you are embarking on your first machine learning project or looking to enhance your existing AI solutions, this book is your gateway to mastering machine learning operations.