Book Image

Learn Amazon SageMaker

By : Julien Simon
Book Image

Learn Amazon SageMaker

By: Julien Simon

Overview of this book

Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation.

Related Content you might be interested in

Current Title:

Small book image
Learn Amazon SageMaker
Julien Simon
Aug, 2020 | 490 pages
Small book image
Machine Learning with Amazon SageMaker Cookbook
Joshua Arvin Lat
Oct, 2021 | 762 pages
Small book image
Accelerate Deep Learning Workloads with Amazon SageMaker
Vadim Dabravolski
Oct, 2022 | 278 pages
Small book image
Getting Started with Amazon SageMaker Studio
Michael Hsieh
Mar, 2022 | 326 pages
Table of Contents (19 chapters)
Preface
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
Get in touch
Reviews
1
Section 1: Introduction to Amazon SageMaker
Section 1: Introduction to Amazon SageMaker
Free Chapter
2
Chapter 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
3
Chapter 2: Handling Data Preparation Techniques
Chapter 2: Handling Data Preparation Techniques
Technical requirements
Discovering Amazon SageMaker Ground Truth
Exploring Amazon SageMaker Processing
Processing data with other AWS services
Summary
4
Section 2: Building and Training Models
Section 2: Building and Training Models
5
Chapter 3: AutoML with Amazon SageMaker Autopilot
Chapter 3: AutoML with Amazon SageMaker Autopilot
Technical requirements
Discovering Amazon SageMaker Autopilot
Using SageMaker Autopilot in SageMaker Studio
Using the SageMaker Autopilot SDK
Diving deep on SageMaker Autopilot
Summary
6
Chapter 4: Training Machine Learning Models
Chapter 4: Training Machine Learning Models
Technical requirements
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
Summary
7
Chapter 5: Training Computer Vision Models
Chapter 5: Training Computer Vision Models
Technical requirements
Discovering the CV built-in algorithms in Amazon SageMaker
Preparing image datasets
Using the built-in CV algorithms
Summary
8
Chapter 6: Training Natural Language Processing Models
Chapter 6: Training Natural Language Processing Models
Technical requirements
Discovering the NLP built-in algorithms in Amazon SageMaker
Preparing natural language datasets
Using the built-in algorithms for NLP
Summary
9
Chapter 7: Extending Machine Learning Services Using Built-In Frameworks
Chapter 7: Extending Machine Learning Services Using Built-In Frameworks
Technical requirements
Discovering the built-in frameworks in Amazon SageMaker
Running your framework code on Amazon SageMaker
Using the built-in frameworks
Summary
10
Chapter 8: Using Your Algorithms and Code
Chapter 8: Using Your Algorithms and Code
Technical requirements
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
Summary
11
Section 3: Diving Deeper on Training
Section 3: Diving Deeper on Training
12
Chapter 9: Scaling Your Training Jobs
Chapter 9: Scaling Your Training Jobs
Technical requirements
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
Summary
13
Chapter 10: Advanced Training Techniques
Chapter 10: Advanced Training Techniques
Technical requirements
Optimizing training costs with Managed Spot Training
Optimizing hyperparameters with Automatic Model Tuning
Exploring models with SageMaker Debugger
Summary
14
Section 4: Managing Models in Production
Section 4: Managing Models in Production
15
Chapter 11: Deploying Machine Learning Models
Chapter 11: Deploying Machine Learning Models
Technical requirements
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
Summary
16
Chapter 12: Automating Machine Learning Workflows
Chapter 12: Automating Machine Learning Workflows
Technical requirements
Automating with AWS CloudFormation
Automating with the AWS Cloud Development Kit
Automating with AWS Step Functions
Summary
17
Chapter 13: Optimizing Prediction Cost and Performance
Chapter 13: Optimizing Prediction Cost and Performance
Technical requirements
Autoscaling an endpoint
Deploying a multi-model endpoint
Deploying a model with Amazon Elastic Inference
Compiling models with Amazon SageMaker Neo
Building a cost optimization checklist
Summary
18
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

What this book covers

Chapter 1, Getting Started with Amazon SageMaker, provides an overview of Amazon SageMaker, what its capabilities are, and how it helps solve many pain points faced by ML projects today.

Chapter 2, Handling Data Preparation Techniques, discusses data preparation options. Although this it isn't the core subject of the book, data preparation is a key topic in ML, and it should be covered at a high level.

Chapter 3, AutoML with Amazon SageMaker AutoPilot, shows you how to build, train, and optimize ML models automatically with Amazon SageMaker AutoPilot.

Chapter 4, Training Machine Learning Models, shows you how to build and train models using the collection of statistical ML algorithms built into Amazon SageMaker.

Chapter 5, Training Computer Vision Models, shows you how to build and train models using the collection of computer vision algorithms built into Amazon SageMaker.

Chapter 6, Training Natural Language Processing Models, shows you how to build and train models using the collection of natural language processing algorithms built into Amazon SageMaker.

Chapter 7, Extending Machine Learning Services Using Built-In Frameworks, shows you how to build and train ML models using the collection of built-in open source frameworks in Amazon SageMaker.

Chapter 8, Using Your Algorithms and Code, shows you how to build and train ML models using your own code on Amazon SageMaker, for example, R or custom Python.

Chapter 9, Scaling Your Training Jobs, shows you how to distribute training jobs to many managed instances, using either built-in algorithms or built-in frameworks.

Chapter 10, Advanced Training Techniques, shows you how to leverage advanced training in Amazon SageMaker.

Chapter 11, Deploying Machine Learning Models, shows you how to deploy ML models in a variety of configurations.

Chapter 12, Automating Deployment Tasks, shows you how to automate the deployment of ML models on Amazon SageMaker.

Chapter 13, Optimizing Cost and Performance, shows you how to optimize model deployments, both from an infrastructure perspective and from a cost perspective.

Previous Section
Next Section