更新时间：2021-04-09 23:11:46
coverpage
Learn Amazon SageMaker
Why subscribe?
Contributors
About the author
About the reviewers
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Foreword
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
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Section 1: Introduction to Amazon SageMaker
Chapter 1: Introduction to Amazon SageMaker
Technical requirements
Exploring the capabilities of Amazon SageMaker
Demonstrating the strengths of Amazon SageMaker
Setting up Amazon SageMaker on your local machine
Setting up an Amazon SageMaker notebook instance
Setting up Amazon SageMaker Studio
Summary
Chapter 2: Handling Data Preparation Techniques
Discovering Amazon SageMaker Ground Truth
Exploring Amazon SageMaker Processing
Processing data with other AWS services
Section 2: Building and Training Models
Chapter 3: AutoML with Amazon SageMaker Autopilot
Discovering Amazon SageMaker Autopilot
Using SageMaker Autopilot in SageMaker Studio
Using the SageMaker Autopilot SDK
Diving deep on SageMaker Autopilot
Chapter 4: Training Machine Learning Models
Discovering the built-in algorithms in Amazon SageMaker
Training and deploying models with built-in algorithms
Using the SageMaker SDK with built-in algorithms
Working with more built-in algorithms
Chapter 5: Training Computer Vision Models
Discovering the CV built-in algorithms in Amazon SageMaker
Preparing image datasets
Using the built-in CV algorithms
Chapter 6: Training Natural Language Processing Models
Discovering the NLP built-in algorithms in Amazon SageMaker
Preparing natural language datasets
Using the built-in algorithms for NLP
Chapter 7: Extending Machine Learning Services Using Built-In Frameworks
Discovering the built-in frameworks in Amazon SageMaker
Running your framework code on Amazon SageMaker
Using the built-in frameworks
Chapter 8: Using Your Algorithms and Code
Understanding how SageMaker invokes your code
Using the SageMaker training toolkit with scikit-learn
Building a fully custom container for scikit-learn
Building a fully custom container for R
Training and deploying with XGBoost and MLflow
Training and deploying with XGBoost and Sagify
Section 3: Diving Deeper on Training
Chapter 9: Scaling Your Training Jobs
Understanding when and how to scale
Streaming datasets with pipe mode
Using other storage services
Distributing training jobs
Training an Image Classification model on ImageNet
Chapter 10: Advanced Training Techniques
Optimizing training costs with Managed Spot Training
Optimizing hyperparameters with Automatic Model Tuning
Exploring models with SageMaker Debugger
Section 4: Managing Models in Production
Chapter 11: Deploying Machine Learning Models
Examining model artifacts
Managing real-time endpoints
Deploying batch transformers
Deploying inference pipelines
Monitoring predictions with Amazon SageMaker Model Monitor
Deploying models to container services
