Apress | Deploy Machine Learning Models To Production: With Flask, Streamlit, Docker, And Kubernetes On Google Cloud Platform (2021 EN)

Author: Pramod Singh
Full Title: Deploy Machine Learning Models To Production: With Flask, Streamlit, Docker, And Kubernetes On Google Cloud Platform
Publisher: Apress; 1st ed. edition (December 29, 2020)
Year: 2021
ISBN-13: 9781484265468 (978-1-4842-6546-8), 9781484265451 (978-1-4842-6545-1)
ISBN-10: 1484265467, 1484265459
Pages: 150
Language: English
Genre: Educational: Machine Learning
File type: EPUB (True), PDF (True)
Quality: 10/10
Price: 32.09 €


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.


Learn:
✓ 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

Features:
✓ Guides you in transitioning from traditional machine learning to machine learning productionization
✓ Covers the entire range of deployment options, including Flask, Streamlit, Docker, and Kubernetes
✓ Presents the process to wrap and containerize any machine learning model

Who This Book Is For:
Data engineers, data scientists, analysts, and machine learning and deep learning engineers.

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