michael katzenellenbogen josh patterson austin (12 Ergebnisse)

Paperback. Zustand: Good. 1. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

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Paperback. Zustand: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Zustand: New.

Paperback. Zustand: New. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premise Kubernetes clusterDeploy Kubeflow on Google Cloud Platform, AWS, and AzureUse KFServing to develop and deploy machine learning models.

Paperback. Zustand: New. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premise Kubernetes clusterDeploy Kubeflow on Google Cloud Platform, AWS, and AzureUse KFServing to develop and deploy machine learning models.

Zustand: New.

Paperback. Zustand: New. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premise Kubernetes clusterDeploy Kubeflow on Google Cloud Platform, AWS, and AzureUse KFServing to develop and deploy machine learning models.

Zustand: New. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.&Uumlber den AutorrnrnJosh Patterson is CEO of Pa.

Zustand: Gut. Zustand: Gut | Seiten: 301 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Paperback. Zustand: New. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premise Kubernetes clusterDeploy Kubeflow on Google Cloud Platform, AWS, and AzureUse KFServing to develop and deploy machine learning models.