Building Machine Learning Powered Applications

Going from Idea to Product

Paperback Engels 2020 1e druk 9781492045113
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Samenvatting

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.

Specificaties

ISBN13:9781492045113
Taal:Engels
Bindwijze:paperback
Aantal pagina's:250
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:31-1-2020
Hoofdrubriek:IT-management / ICT

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Over Emmanuel Ameisen

Emmanuel Ameisen, a machine learning engineer at Stripe, implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, he led Insight Data Science’s AI program, directing more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.

Andere boeken door Emmanuel Ameisen

Inhoudsopgave

Preface
The Goal of Using Machine Learning Powered Applications
Use ML to Build Practical Applications
Additional Resources
Practical ML
What This Book Covers
Prerequisites
Our Case Study: ML–Assisted Writing
The ML Process
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments

I. Find the Correct ML Approach
1. From Product Goal to ML Framing
Estimate What Is Possible
Models
Data
Framing the ML Editor
Trying to Do It All with ML: An End-to-End Framework
The Simplest Approach: Being the Algorithm
Middle Ground: Learning from Our Experience
Monica Rogati: How to Choose and Prioritize ML Projects
Conclusion

2. Create a Plan
Measuring Success
Business Performance
Model Performance
Freshness and Distribution Shift
Speed
Estimate Scope and Challenges
Leverage Domain Expertise
Stand on the Shoulders of Giants
ML Editor Planning
Initial Plan for an Editor
Always Start with a Simple Model
To Make Regular Progress: Start Simple
Start with a Simple Pipeline
Pipeline for the ML Editor
Conclusion

II. Build a Working Pipeline
3. Build Your First End-to-End Pipeline
The Simplest Scaffolding
Prototype of an ML Editor
Parse and Clean Data
Tokenizing Text
Generating Features
Test Your Workflow
User Experience
Modeling Results
ML Editor Prototype Evaluation
Model
User Experience
Conclusion

4. Acquire an Initial Dataset
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