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Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.

Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.

Understand the steps to build a machine learning pipeline
Build your pipeline using components from TensorFlow Extended
Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines
Work with data using TensorFlow Data Validation and TensorFlow Transform
Analyze a model in detail using TensorFlow Model Analysis
Examine fairness and bias in your model performance
Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices
Learn privacy-preserving machine learning techniques



Table of contents

Foreword
Preface
What Are Machine Learning Pipelines?
Who Is This Book For?
Why TensorFlow and TensorFlow Extended?
Overview of the Chapters
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
1. Introduction
Why Machine Learning Pipelines?
When to Think About Machine Learning Pipelines
Overview of the Steps in a Machine Learning Pipeline
Data Ingestion and Data Versioning
Data Validation
Data Preprocessing
Model Training and Tuning
Model Analysis
Model Versioning
Model Deployment
Feedback Loops
Data Privacy
Pipeline Orchestration
Why Pipeline Orchestration?
Directed Acyclic Graphs
Our Example Project
Project Structure
Our Machine Learning Model
Goal of the Example Project
Summary
2. Introduction to TensorFlow Extended
What Is TFX?
Installing TFX
Overview of TFX Components
What Is ML Metadata?
Interactive Pipelines
Alternatives to TFX
Introduction to Apache Beam
Setup
Basic Data Pipeline
Executing Your Basic Pipeline
Summary
3. Data Ingestion
Concepts for Data Ingestion
Ingesting Local Data Files
Ingesting Remote Data Files
Ingesting Data Directly from Databases
Data Preparation
Splitting Datasets
Spanning Datasets
Versioning Datasets
Ingestion Strategies
Structured Data
Text Data for Natural Language Problems
Image Data for Computer Vision Problems
Summary
4. Data Validation
Why Data Validation?
TFDV
Installation
Generating Statistics from Your Data
Generating Schema from Your Data
Recognizing Problems in Your Data
Comparing Datasets
Updating the Schema
Data Skew and Drift
Biased Datasets
Slicing Data in TFDV
Processing Large Datasets with GCP
Integrating TFDV into Your Machine Learning Pipeline
Summary
5. Data Preprocessing
Why Data Preprocessing?
Preprocessing the Data in the Context of the Entire Dataset
Scaling the Preprocessing Steps
Avoiding a Training-Serving Skew
Deploying Preprocessing Steps and the ML Model as One Artifact
Checking Your Preprocessing Results in Your Pipeline
Data Preprocessing with TFT
Installation
Preprocessing Strategies
Best Practices
TFT Functions
Standalone Execution of TFT
Integrate TFT into Your Machine Learning Pipeline
Summary
6. Model Training
Defining the Model for Our Example Project
The TFX Trainer Component
run_fn() Function
Running the Trainer Component
Other Trainer Component Considerations
Using TensorBoard in an Interactive Pipeline
Distribution Strategies
Model Tuning
Strategies for Hyperparameter Tuning
Hyperparameter Tuning in TFX Pipelines
Summary
7. Model Analysis and Validation
How to Analyze Your Model
Classification Metrics
Regression Metrics
TensorFlow Model Analysis
Analyzing a Single Model in TFMA
Analyzing Multiple Models in TFMA
Model Analysis for Fairness
Slicing Model Predictions in TFMA
Checking Decision Thresholds with Fairness Indicators
Going Deeper with the What-If Tool
Model Explainability
Generating Explanations with the WIT
Other Explainability Techniques
Analysis and Validation in TFX
ResolverNode
Evaluator Component
Validation in the Evaluator Component
TFX Pusher Component
Summary
8. Model Deployment with TensorFlow Serving
A Simple Model Server
The Downside of Model Deployments with Python-Based APIs
Lack of Code Separation
Lack of Model Version Control
Inefficient Model Inference
TensorFlow Serving
TensorFlow Architecture Overview
Exporting Models for TensorFlow Serving
Model Signatures
Inspecting Exported Models
Setting Up TensorFlow Serving
Docker Installation
Native Ubuntu Installation
Building TensorFlow Serving from Source
Configuring a TensorFlow Server
REST Versus gRPC
Making Predictions from the Model Server
Getting Model Predictions via REST
Using TensorFlow Serving via gRPC
Model A/B Testing with TensorFlow Serving
Requesting Model Metadata from the Model Server
REST Requests for Model Metadata
gRPC Requests for Model Metadata
Batching Inference Requests
Configuring Batch Predictions
Other TensorFlow Serving Optimizations
TensorFlow Serving Alternatives
BentoML
Seldon
GraphPipe
Simple TensorFlow Serving