TY  - GEN
N2  - 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.
DO  - 10.1007/978-1-4842-6546-8
DO  - doi
AB  - 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.
T1  - Deploy machine learning models to production :with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
DA  - 2021.
CY  - Berkeley, CA :
AU  - Singh, Pramod.
CN  - Q325.5
PB  - Apress L.P.,
PP  - Berkeley, CA :
PY  - 2021.
ID  - 1432981
KW  - Machine learning.
KW  - Python (Computer program language)
KW  - Open source software.
KW  - Computer programming.
KW  - Apprentissage automatique.
KW  - Python (Langage de programmation)
KW  - Logiciels libres.
KW  - Programmation (Informatique)
SN  - 9781484265468
SN  - 1484265467
SN  - 9781484265475
SN  - 1484265475
TI  - Deploy machine learning models to production :with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6546-8
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6546-8
ER  -