Building Machine Learning Powered Applications: Going from Idea to Product


Building Machine Learning Powered Applications: Going from Idea to Product
By 作者: Emmanuel Ameisen
ISBN-10 书号: 149204511X
ISBN-13 书号: 9781492045113
Edition 版本: 1
Release Finelybook 出版日期: 2020-02-04
pages 页数: (260 )

The Book Description robot was collected from Amazon and arranged by Finelybook
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). 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—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.
Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:

Define 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 models and address performance bottlenecks
Deploy and monitor your models in a production environment
Preface
1.Find the Correct ML Approach
1.From Product Goal to ML Framing
2.Create a Plan
lⅡ.Build a Working Pipeline
3.Build Your First End-to-End Pipeline
4.Acquire an Initial Dataset
ll.Iterate on Models
5.Train and Evaluate Your Model
6.Debug Your ML Problems
7.Using Classifiers for Writing Recommendations
IV.Deploy and Monitor
8.Considerations When Deploying Models
9.Choose Your Deployment Option
10.Build Safeguards for Models
11.Monitor and Update Models
Index

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