Deep Learning in Production

Book Description

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

Deep Learning research is advancing rapidly over the past years. Frameworks and libraries are constantly been developed and updated. However, we still lack standardized solutions on how to serve, deploy and scale Deep Learning models. Deep Learning infrastructure is not very mature yet.

This book accumulates a set of best practices and approaches on how to build robust and scalable machine learning applications. It covers the entire lifecycle from data processing and training to deployment and maintenance. It will help you understand how to transfer methodologies that are generally accepted and applied in the software community, into Deep Learning projects.

It’s an excellent choice for researchers with a minimal software background, software engineers with little experience in machine learning, or aspiring machine learning engineers.

What you will learn?

• Best practices to write Deep Learning code
• How to unit test and debug Machine Learning code
• How to build and deploy efficient data pipelines
• How to serve Deep Learning models
• How to deploy and scale your application
• What is MLOps and how to build end-to-end pipelines

Who is this book for?

• Software engineers who are starting out with deep learning
• Machine learning researchers with limited software engineering background
• Machine learning engineers who seek to strengthen their knowledge
• Data scientists who want to productionize their models and build customer-facing applications

What tools you will use?

Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AI

中文：

书名：生产中的深度学习

构建、培训、部署、扩展和维护深度学习模型。使用动手示例了解ML基础架构和MLOps。

在过去的几年中，深度学习研究正在迅速发展。框架和库不断开发和更新。但是，在如何服务，部署和扩展深度学习模型方面，我们仍然缺乏标准化的解决方案。深度学习基础设施还不太成熟。

本书积累了一套关于如何构建健壮和可扩展的机器学习应用程序的最佳实践和方法。它涵盖了从数据处理和培训到部署和维护的整个生命周期。它将帮助您了解如何将软件社区中普遍接受和应用的方法转移到深度学习项目中。

对于具有最少软件背景的研究人员，几乎没有机器学习经验的软件工程师或有抱负的机器学习工程师来说，这是一个绝佳的选择。

你会学到什么？

• 编写深度学习代码的最佳实践
• 如何进行单元测试和调试机器学习代码
• 如何构建和部署高效的数据管道
• 如何服务深度学习模型
• 如何部署和扩展应用
• 什么是MLOps，如何构建端到端管道

这本书是给谁的？

• 开始深度学习的软件工程师
• 机器学习研究人员有限的软件工程背景
• 寻求加强知识的机器学习工程师
• 数据科学家想要生产他们的模型和构建面向客户的应用程序

你将使用什么工具？

Tensorflow、Flask、uWSGI、Nginx、Docker、Kubernetes、Tensorflow Extended、Google Cloud、Vertex AI