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 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

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

通过端到端的例子在生产中构建和部署机器学习和深度学习模型。

本书首先重点介绍了机器学习模型的部署过程及其相关挑战。接下来，它涵盖了使用Flask和Streamlit等不同网络框架构建和部署机器学习模型的过程。接下来是关于Docker的一章，涵盖了如何打包和容器化机器学习模型。该书还说明了如何使用Kubernetes大规模地构建和训练机器学习和深度学习模型。

对于那些想通过采用预建模型并将其部署到生产中而进入机器学习下一阶段的人来说，这本书是一个很好的起点。它还为那些想超越Jupyter笔记本，在云环境中大规模训练模型的人提供指导。书中介绍的所有代码都是以Python脚本的形式提供的，你可以尝试这些例子并以有趣的方式扩展它们。

你将会学到什么

使用Kubernetes大规模构建、训练和部署机器学习模型

容器化任何类型的机器学习模型，并使用Docker在任何平台上运行它

使用Flask和Streamlit框架部署机器学习和深度学习模型

本书适用对象

数据工程师、数据科学家、分析师以及机器学习和深度学习工程师