Building Machine Learning Pipelines Hannes Hapke, Catherine Nelson

Dostępność produktu

Produkt nie został jeszcze oceniony pod kątem ułatwień dostępu lub nie podano żadnych informacji o ułatwieniach dostępu lub są one niewystarczające. Prawdopodobnie Wydawca/Dostawca jeszcze nie umożliwił dokonania walidacji produktu lub nie przekazał odpowiednich informacji na temat jego dostępności.

Spis treści książki

• 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
  • OReilly 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
      • Basic collection example
      • Basic transformation example
      • Putting it all together
    • Executing Your Basic Pipeline
  • Summary
• 3. Data Ingestion
  • Concepts for Data Ingestion
    • Ingesting Local Data Files
      • Converting comma-separated data to tf.Example
      • Importing existing TFRecord Files
      • Converting Parquet-serialized data to tf.Example
      • Converting Avro-serialized data to tf.Example
      • Converting your custom data to TFRecord data structures
    • Ingesting Remote Data Files
    • Ingesting Data Directly from Databases
      • Google Cloud BigQuery
      • Presto databases
  • Data Preparation
    • Splitting Datasets
      • Splitting one dataset into subsets
      • Preserving existing splits
    • 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
      • Text data for natural language problems
      • Image data for computer vision problems
    • 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
      • Load the data
      • Compile and train the model
      • Model export
    • Running the Trainer Component
    • Other Trainer Component Considerations
      • Using the Trainer component with an Estimator model
      • Using the SavedModel outside a pipeline
  • 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
      • URL structure
      • Payloads
    • Using TensorFlow Serving via gRPC
      • Getting predictions from classification and regression models
      • Payloads
  • 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
    • MLflow
    • Ray Serve
  • Deploying with Cloud Providers
    • Use Cases
    • Example Deployment with GCP
      • Model deployment
      • Model inference
  • Model Deployment with TFX Pipelines
  • Summary
• 9. Advanced Model Deployments with TensorFlow Serving
  • Decoupling Deployment Cycles
    • Workflow Overview
      • Accessing private models from AWS S3
      • Accessing private models from GCP Buckets
    • Optimization of Remote Model Loading
  • Model Optimizations for Deployments
    • Quantization
    • Pruning
    • Distillation
  • Using TensorRT with TensorFlow Serving
  • TFLite
    • Steps to Optimize Your Model with TFLite
    • Serving TFLite Models with TensorFlow Serving
  • Monitoring Your TensorFlow Serving Instances
    • Prometheus Setup
    • TensorFlow Serving Configuration
  • Simple Scaling with TensorFlow Serving and Kubernetes
  • Summary
• 10. Advanced TensorFlow Extended
  • Advanced Pipeline Concepts
    • Training Multiple Models Simultaneously
    • Exporting TFLite Models
    • Warm Starting Model Training
  • Human in the Loop
    • Slack Component Setup
    • How to Use the Slack Component
  • Custom TFX Components
    • Use Cases of Custom Components
    • Writing a Custom Component from Scratch
      • Component specifications
      • Component channels
      • Component executors
      • Component drivers
      • Assembling the custom component
      • Using our basic custom component
      • Implementation review
    • Reusing Existing Components
      • Using our custom executor
  • Summary
• 11. Pipelines Part 1: Apache Beam and Apache Airflow
  • Which Orchestration Tool to Choose?
    • Apache Beam
    • Apache Airflow
    • Kubeflow Pipelines
    • Kubeflow Pipelines on AI Platform
  • Converting Your Interactive TFX Pipeline to a Production Pipeline
  • Simple Interactive Pipeline Conversion for Beam and Airflow
  • Introduction to Apache Beam
  • Orchestrating TFX Pipelines with Apache Beam
  • Introduction to Apache Airflow
    • Installation and Initial Setup
    • Basic Airflow Example
      • Project-specific configurations
      • Task definitions
      • Task dependencies
      • Putting it all together
  • Orchestrating TFX Pipelines with Apache Airflow
    • Pipeline Setup
    • Pipeline Execution
  • Summary
• 12. Pipelines Part 2: Kubeflow Pipelines
  • Introduction to Kubeflow Pipelines
    • Installation and Initial Setup
    • Accessing Your Kubeflow Pipelines Installation
  • Orchestrating TFX Pipelines with Kubeflow Pipelines
    • Pipeline Setup
    • Executing the Pipeline
    • Useful Features of Kubeflow Pipelines
      • Restart failed pipelines
      • Recurring runs
      • Collaborating and reviewing pipeline runs
      • Auditing the pipeline lineage
  • Pipelines Based on Google Cloud AI Platform
    • Pipeline Setup
    • TFX Pipeline Setup
      • Use Cloud Storage buckets for data exchange
      • Training models with an AI Platform job
      • Serving models through AI Platform endpoints
      • Scaling with Googles Dataflow
    • Pipeline Execution
  • Summary
• 13. Feedback Loops
  • Explicit and Implicit Feedback
    • The Data Flywheel
    • Feedback Loops in the Real World
  • Design Patterns for Collecting Feedback
    • Users Take Some Action as a Result of the Prediction
    • Users Rate the Quality of the Prediction
    • Users Correct the Prediction
    • Crowdsourcing the Annotations
    • Expert Annotations
    • Producing Feedback Automatically
  • How to Track Feedback Loops
    • Tracking Explicit Feedback
    • Tracking Implicit Feedback
  • Summary
• 14. Data Privacy for Machine Learning
  • Data Privacy Issues
    • Why Do We Care About Data Privacy?
    • The Simplest Way to Increase Privacy
    • What Data Needs to Be Kept Private?
  • Differential Privacy
    • Local and Global Differential Privacy
    • Epsilon, Delta, and the Privacy Budget
    • Differential Privacy for Machine Learning
  • Introduction to TensorFlow Privacy
    • Training with a Differentially Private Optimizer
    • Calculating Epsilon
  • Federated Learning
    • Federated Learning in TensorFlow
  • Encrypted Machine Learning
    • Encrypted Model Training
    • Converting a Trained Model to Serve Encrypted Predictions
  • Other Methods for Data Privacy
  • Summary
• 15. The Future of Pipelines and Next Steps
  • Model Experiment Tracking
  • Thoughts on Model Release Management
  • Future Pipeline Capabilities
  • TFX with Other Machine Learning Frameworks
  • Testing Machine Learning Models
  • CI/CD Systems for Machine Learning
  • Machine Learning Engineering Community
  • Summary
• A. Introduction to Infrastructure for Machine Learning
  • What Is a Container?
  • Introduction to Docker
    • Introduction to Docker Images
    • Building Your First Docker Image
    • Diving into the Docker CLI
  • Introduction to Kubernetes
    • Some Kubernetes Definitions
    • Getting Started with Minikube and kubectl
    • Interacting with the Kubernetes CLI
    • Defining a Kubernetes Resource
  • Deploying Applications to Kubernetes
• B. Setting Up a Kubernetes Cluster on Google Cloud
  • Before You Get Started
  • Kubernetes on Google Cloud
    • Selecting a Google Cloud Project
    • Setting Up Your Google Cloud Project
    • Creating a Kubernetes Cluster
    • Accessing Your Kubernetes Cluster with kubectl
    • Using Your Kubernetes Cluster with kubectl
  • Persistent Volume Setups for Kubeflow Pipelines
• C. Tips for Operating Kubeflow Pipelines
  • Custom TFX Images
  • Exchange Data Through Persistent Volumes
  • TFX Command-Line Interface
    • TFX and Its Dependencies
    • TFX Templates
    • Publishing Your Pipeline with TFX CLI
• Index

pokaż cały spis treści

ukryj recenzje