deploy machine learning models de singh pramod (24 resultados)

Condición: Good. 2020. Paperback. Ex Libris with usual markings. Clean copy with some shelf wear, some sunning and nicks and tears to cover, inscribed by previous owner, otherwise a good copy. . . . . Books ship from the US and Ireland.

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Condición: Good. 2020. Paperback. Ex Libris with usual markings. Clean copy with some shelf wear, some sunning and nicks and tears to cover, inscribed by previous owner, otherwise a good copy. . . . .

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers Intermediate-Advanced user level Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condición: New. 1st ed. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers.

Paperback. Condición: New. 1st ed. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers.

Paperback. Condición: Brand New. 150 pages. 9.00x6.00x0.50 inches. In Stock.

Condición: New. In.

PF. Condición: New.

Condición: New. 1st ed. edition NO-PA16APR2015-KAP.

Paperback. Condición: New. 1st ed. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers.

Paperback. Condición: new. Paperback. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers Intermediate-Advanced user level Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. Neuware -Build and deploy machine learning and deep learning models in production with end-to-end examples.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 164 pp. Englisch.

Paperback. Condición: New. 1st ed. Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter not Elektronisches Buch to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers 164 pp. Englisch.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate-Advanced user level|Guides you in transitioning from traditional machine learning to machine learning productionizationCovers the entire range of deployment options, including Flask, Streamlit, Docker, and Kubernetes&nbsp.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build and deploy machine learning and deep learning models in production with end-to-end examples.This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter not Elektronisches Buch to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways.What You Will LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers.

Taschenbuch. Condición: Neu. Deploy Machine Learning Models to Production | With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform | Pramod Singh | Taschenbuch | xiii | Englisch | 2020 | Apress | EAN 9781484265451 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.