Building Machine Learning Powered Applications: Going from Idea to Product

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Building Machine Learning Powered Applications: Going from Idea to Product

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內容描述

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

目錄大綱

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
   Iterate on Datasets
   Do Data Science
   Explore Your First Dataset
   Be Efficient, Start Small
   Insights Versus Products
   A Data Quality Rubric
   Label to Find Data Trends
   Summary Statistics
   Explore and Label Efficiently
   Be the Algorithm
   Data Trends
   Let Data Inform Features and Models
   Build Features Out of Patterns
   ML Editor Features
   Robert Munro: How Do You Find, Label, and Leverage Data?
   Conclusion
   III. Iterate on Models
5. Train and Evaluate Your Model
   The Simplest Appropriate Model
   Simple Models
   From Patterns to Models
   Split Your Dataset
   ML Editor Data Split
   Judge Performance
   Evaluate Your Model: Look Beyond Accuracy
   Contrast Data and Predictions
   Confusion Matrix
   ROC Curve
   Calibration Curve
   Dimensionality Reduction for Errors
   The Top-k Method
   Other Models
   Evaluate Feature Importance
   Directly from a Classifier
   Black-Box Explainers
   Conclusion
6. Debug Your ML Problems
   Software Best Practices
   ML-Specific Best Practices
   Debug Wiring: Visualizing and Testing
   Start with One Example
   Test Your ML Code
   Debug Training: Make Your Model Learn
   Task Difficulty
   Optimization Problems
   Debug Generalization: Make Your Model Useful
   Data Leakage
   Overfitting
   Consider the Task at Hand
   Conclusion
7. Using Classifiers for Writing Recommendations
   Extracting Recommendations from Models
   What Can We Achieve Without a Model?
   Extracting Global Feature Importance
   Using a Model’s Score
   Extracting Local Feature Importance
   Comparing Models
   Version 1: The Report Card
   Version 2: More Powerful, More Unclear
   Version 3: Understandable Recommendations
   Generating Editing Recommendations
   Conclusion
   IV. Deploy and Monitor
8. Considerations When Deploying Models
   Data Concerns
   Data Ownership
   Data Bias
   Systemic Bias
   Modeling Concerns
   Feedback Loops
   Inclusive Model Performance
   Considering Context
   Adversaries
   Abuse Concerns and Dual-Use
   Chris Harland: Shipping Experiments
   Conclusion
9. Choose Your Deployment Option
   Server-Side Deployment
   Streaming Application or API
   Batch Predictions
   Client-Side Deployment
   On Device
   Browser Side
   Federated Learning: A Hybrid Approach
   Conclusion
10. Build Safeguards for Models
    Engineer Around Failures
    Input and Output Checks
    Model Failure Fallbacks
    Engineer for Performance
    Scale to Multiple Users
    Model and Data Life Cycle Management
    Data Processing and DAGs
    Ask for Feedback
    Chris Moody: Empowering Data Scientists to Deploy Models
    Conclusion
11. Monitor and Update Models
    Monitoring Saves Lives
    Monitoring to Inform Refresh Rate
    Monitor to Detect Abuse
    Choose What to Monitor
    Performance Metrics
    Business Metrics
    CI/CD for ML
    A/B Testing and Experimentation
    Other Approaches
    Conclusion
    Index

作者介紹

Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science's AI program where he oversaw 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.