Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.
Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science’s AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book’s example application.
The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
* Determine your product goal and set up a machine learning problem
* Build your first end-to-end pipeline quickly and acquire an initial dataset
* Train and evaluate your ML model and address performance bottlenecks

* Deploy and monitor models in a production environment

学习设计、构建和部署由机器学习驱动的应用程序的必要技能。通过这本实践书的学习，你将构建一个由ML驱动的应用实例，从最初的想法到部署产品。几乎没有ML经验的数据科学家、软件工程师和产品经理将逐步学习构建一个真实世界的ML应用所涉及的工具、最佳实践和挑战。

作者Emmanuel Ameisen曾在Zipcar担任数据科学家，并领导Insight Data Science的人工智能项目，他通过代码片段、插图和书中示例应用的屏幕截图演示了关键的ML概念。

本指南的第一部分向你展示了如何计划和衡量一个ML应用的成功。第二部分告诉你如何建立一个工作的ML模型，第三部分解释了如何改进模型，直到它实现你最初的设想。第四部分包括部署和监控策略。

本书将帮助你。

* 确定你的产品目标并设置机器学习问题

* 迅速建立你的第一个端到端管道，并获得一个初始数据集

* 训练和评估你的ML模型并解决性能瓶颈问题

* 在生产环境中部署和监控模型