國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deploy machine learning models to pr...

Singh, Pramod.

Deploy machine learning models to productionwith Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deploy machine learning models to productionby Pramod Singh.
其他題名:	with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
作者:	Singh, Pramod.
出版者:	Berkeley, CA :Apress :2021.
面頁冊數:	xiii, 150 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-1-4842-6546-8
ISBN:	9781484265468$q(electronic bk.)

Deploy machine learning models to productionwith 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. - Berkeley, CA :Apress :2021. - xiii, 150 p. :ill., digital ;24 cm.

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$q(electronic bk.)

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

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Deploy machine learning models to productionwith Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
LDR:02563nmm a2200325 a 4500 001 596670
003 DE-He213
005 20201214102316.0
006 m d
007 cr nn 008maaau
008 211013s2021 cau s 0 eng d
020 $a 9781484265468$q(electronic bk.)
020 $a 9781484265451$q(paper)
024 7 $a 10.1007/978-1-4842-6546-8 $2 doi
035 $a 978-1-4842-6546-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S617 2021
100 1 $a Singh, Pramod. $3 838479
245 1 0 $a Deploy machine learning models to production $h [electronic resource] : $b with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform / $c by Pramod Singh.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xiii, 150 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 0 $a Python (Computer program language) $3 215247
650 0 $a Open source software. $3 200208
650 0 $a Computer programming. $3 181992
650 2 4 $a Python. $3 763308
650 2 4 $a Open Source. $3 758930
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6546-8
950 $a Professional and Applied Computing (SpringerNature-12059)