國立虎尾科技大學 |

Deploy Machine Learning Models to Production = With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deploy Machine Learning Models to Production/ by Pramod Singh.
其他題名:	With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
作者:	Singh, Pramod.
面頁冊數:	XIII, 150 p. 115 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Open Source. -
電子資源:	https://doi.org/10.1007/978-1-4842-6546-8
ISBN:	9781484265468

Deploy Machine Learning Models to Production = With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
Singh, Pramod.

Deploy Machine Learning Models to ProductionWith Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /[electronic resource] :by Pramod Singh. - 1st ed. 2021. - XIII, 150 p. 115 illus.online resource.

Chapter 1: Introduction to Machine Learning -- Chapter 2: Model Deployment and Challenges -- Chapter 3: Machine Learning Deployment as a Web Service -- Chapter 4: Machine Learning Deployment Using Docker -- Chapter 5: Machine Learning Deployment Using Kubernetes.

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. You will: Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks.

ISBN: 9781484265468

Standard No.: 10.1007/978-1-4842-6546-8doiSubjects--Topical Terms:

1113081
Open Source.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Deploy Machine Learning Models to Production = With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
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