Learn Amazon SageMaker : a guide to building, training, and deploying machine learning models for developers and data scientists / Julien Simon.

Format:

Book

Author/Creator:

Simon, Julien, 1952- author.

Language:

English

Subjects (All):

Machine learning.

Cloud computing.

Physical Description:

1 online resource (554 pages)

Edition:

Second edition.

Place of Publication:

London, England : Packt Publishing, [2021]

Summary:

Swiftly build and deploy machine learning models without managing infrastructure and boost productivity using the latest Amazon SageMaker capabilities such as Studio, Autopilot, Data Wrangler, Pipelines, and Feature StoreKey FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerOptimize the accuracy, cost, and fairness of your modelsCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Book DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning models at scale without managing any infrastructure. It helps you focus on the machine learning problem at hand and deploy high-quality models by eliminating the heavy lifting typically involved in each step of the ML process. This second edition will help data scientists and ML developers to explore new features such as SageMaker Data Wrangler, Pipelines, Clarify, Feature Store, and much more. You'll start by learning how to use various capabilities of SageMaker as a single toolset to solve ML challenges and progress to cover features such as AutoML, built-in algorithms and frameworks, and writing your own code and algorithms to build ML models. The book will then show you how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how automating your workflows can help you get to production faster with minimum effort and at a lower cost. Finally, you'll explore SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production. 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.What you will learnBecome well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and natural language processing (NLP) models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is forThis book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Section 1: Introduction to Amazon SageMaker

Chapter 1: Introducing Amazon SageMaker

Technical requirements

Exploring the capabilities of Amazon SageMaker

The main capabilities of Amazon SageMaker

The Amazon SageMaker API

Setting up Amazon SageMaker on your local machine

Installing the SageMaker SDK with virtualenv

Installing the SageMaker SDK with Anaconda

A word about AWS permissions

Setting up Amazon SageMaker Studio

Onboarding to Amazon SageMaker Studio

Onboarding with the quick start procedure

Deploying one-click solutions and models with Amazon SageMaker JumpStart

Deploying a solution

Deploying a model

Fine-tuning a model

Summary

Chapter 2: Handling Data Preparation Techniques

Labeling data with Amazon SageMaker Ground Truth

Using workforces

Creating a private workforce

Uploading data for labeling

Creating a labeling job

Labeling images

Labeling text

Transforming data with Amazon SageMaker Data Wrangler

Loading a dataset in SageMaker Data Wrangler

Transforming a dataset in SageMaker Data Wrangler

Exporting a SageMaker Data Wrangler pipeline

Running batch jobs with Amazon SageMaker Processing

Discovering the Amazon SageMaker Processing API

Processing a dataset with scikit-learn

Processing a dataset with your own code

Section 2: Building and Training Models

Chapter 3: AutoML with Amazon SageMaker Autopilot

Discovering Amazon SageMaker Autopilot

Analyzing data

Feature engineering

Model tuning

Using Amazon SageMaker Autopilot in SageMaker Studio

Launching a job

Monitoring a job

Comparing jobs

Deploying and invoking a model.

Using the SageMaker Autopilot SDK

Cleaning up

Diving deep on SageMaker Autopilot

The job artifacts

The data exploration notebook

The candidate generation notebook

Chapter 4: Training Machine Learning Models

Discovering the built-in algorithms in Amazon SageMaker

Supervised learning

Unsupervised learning

A word about scalability

Training and deploying models with built-in algorithms

Understanding the end-to-end workflow

Using alternative workflows

Using fully managed infrastructure

Using the SageMaker SDK with built-in algorithms

Preparing data

Configuring a training job

Launching a training job

Working with more built-in algorithms

Regression with XGBoost

Recommendation with Factorization Machines

Using Principal Component Analysis

Detecting anomalies with Random Cut Forest

Chapter 5: Training CV Models

Discovering the CV built-in algorithms in Amazon SageMaker

Discovering the image classification algorithm

Discovering the object detection algorithm

Discovering the semantic segmentation algorithm

Training with CV algorithms

Preparing image datasets

Working with image files

Working with RecordIO files

Working with SageMaker Ground Truth files

Using the built-in CV algorithms

Training an image classification model

Fine-tuning an image classification model

Training an object detection model

Training a semantic segmentation model

Chapter 6: Training Natural Language Processing Models

Discovering the NLP built-in algorithms in Amazon SageMaker

Discovering the BlazingText algorithm

Discovering the LDA algorithm

Discovering the NTM algorithm.

Discovering the seq2sea algorithm

Training with NLP algorithms

Preparing natural language datasets

Preparing data for classification with BlazingText

Preparing data for classification with BlazingText, version 2

Preparing data for word vectors with BlazingText

Preparing data for topic modeling with LDA and NTM

Using datasets labeled with SageMaker Ground Truth

Using the built-in algorithms for NLP

Classifying text with BlazingText

Computing word vectors with BlazingText

Using BlazingText models with FastText

Modeling topics with LDA

Modeling topics with NTM

Chapter 7: Extending Machine Learning Services Using Built-In Frameworks

Discovering the built-in frameworks in Amazon SageMaker

Running a first example with XGBoost

Working with framework containers

Training and deploying locally

Training with script mode

Understanding model deployment

Managing dependencies

Putting it all together

Running your framework code on Amazon SageMaker

Using the built-in frameworks

Working with TensorFlow and Keras

Working with PyTorch

Working with Hugging Face

Working with Apache Spark

Chapter 8: Using Your Algorithms and Code

Understanding how SageMaker invokes your code

Customizing an existing framework container

Setting up your build environment on EC2

Building training and inference containers

Using the SageMaker Training Toolkit with scikit-learn

Building a fully custom container for scikit-learn

Training with a fully custom container

Deploying a fully custom container

Building a fully custom container for R

Coding with R and plumber

Building a custom container

Training and deploying a custom container on SageMaker

Training and deploying with your own code on MLflow.

Installing MLflow

Training a model with MLflow

Building a SageMaker container with MLflow

Building a fully custom container for SageMaker Processing

Section 3: Diving Deeper into Training

Chapter 9: Scaling Your Training Jobs

Understanding when and how to scale

Understanding what scaling means

Adapting training time to business requirements

Right-sizing training infrastructure

Deciding when to scale

Deciding how to scale

Scaling a BlazingText training job

Monitoring and profiling training jobs with Amazon SageMaker Debugger

Viewing monitoring and profiling information in SageMaker Studio

Enabling profiling in SageMaker Debugger

Solving training challenges

Streaming datasets with pipe mode

Using pipe mode with built-in algorithms

Using pipe mode with other algorithms and frameworks

Simplifying data loading with MLIO

Training factorization machines with pipe mode

Distributing training jobs

Understanding data parallelism and model parallelism

Distributing training for built-in algorithms

Distributing training for built-in frameworks

Distributing training for custom containers

Scaling an image classification model on ImageNet

Preparing the ImageNet dataset

Defining our training job

Training on ImageNet

Updating batch size

Adding more instances

Summing things up

Training with the SageMaker data and model parallel libraries

Training on TensorFlow with SageMaker DDP

Training on Hugging Face with SageMaker DDP

Training on Hugging Face with SageMaker DMP

Using other storage services

Working with SageMaker and Amazon EFS

Working with SageMaker and Amazon FSx for Lustre

Chapter 10: Advanced Training Techniques

Optimizing training costs with managed spot training.

Comparing costs

Understanding Amazon EC2 Spot Instances

Understanding managed spot training

Using managed spot training with object detection

Using managed spot training and checkpointing with Keras

Optimizing hyperparameters with automatic model tuning

Understanding automatic model tuning

Using automatic model tuning with object detection

Using automatic model tuning with Keras

Using automatic model tuning for architecture search

Exploring models with SageMaker Debugger

Debugging an XGBoost job

Inspecting an XGBoost job

Debugging and inspecting a Keras job

Managing features and building datasets with SageMaker Feature Store

Engineering features with SageMaker Processing

Creating a feature group

Ingesting features

Querying features to build a dataset

Exploring other capabilities of SageMaker Feature Store

Detecting bias in datasets and explaining predictions with SageMaker Clarify

Configuring a bias analysis with SageMaker Clarify

Running a bias analysis

Analyzing bias metrics

Running an explainability analysis

Mitigating bias

Section 4: Managing Models in Production

Chapter 11: Deploying Machine Learning Models

Examining model artifacts and exporting models

Examining and exporting built-in models

Examining and exporting built-in CV models

Examining and exporting XGBoost models

Examining and exporting scikit-learn models

Examining and exporting TensorFlow models

Examining and exporting Hugging Face models

Deploying models on real-time endpoints

Managing endpoints with the SageMaker SDK

Managing endpoints with the boto3 SDK

Deploying models on batch transformers

Deploying models on inference pipelines

Monitoring prediction quality with Amazon SageMaker Model Monitor

Capturing data.

Creating a baseline.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781801814157

OCLC:

1281955521