Learn Amazon SageMaker

Julien Simon

• Publisher: Packt Publishing
• Year: 2021
• eBook ISBN: 9781801814157
• Edition: 2
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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 Features• Build, train, and deploy machine learning models quickly using Amazon SageMaker• Optimize the accuracy, cost, and fairness of your models• Create 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 learn• Become well-versed with data annotation and preparation techniques• Use AutoML features to build and train machine learning models with AutoPilot• Create models using built-in algorithms and frameworks and your own code• Train computer vision and natural language processing (NLP) models using real-world examples• Cover training techniques for scaling, model optimization, model debugging, and cost optimization• Automate 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.

Section 1: Introduction to Amazon SageMaker

The objective of this section is to introduce you to the key concepts, help you download supporting data, and introduce you to example scenarios and use cases.

This section comprises the following chapters:

• Chapter 1, Introducing Amazon SageMaker
• Chapter 2, Handling Data Preparation Techniques

Chapter 1: Introducing Amazon SageMaker

Machine learning (ML) practitioners use a large collection of tools in the course of their projects: open source libraries, deep learning frameworks, and more. In addition, they often have to write their own tools for automation and orchestration. Managing these tools and their underlying infrastructure is time-consuming and error-prone.

This is the very problem that Amazon SageMaker was designed to address (https://aws.amazon.com/sagemaker/). Amazon SageMaker is a fully managed service that helps you quickly build and deploy machine learning models. Whether you're just beginning with machine learning or you're an experienced practitioner, you'll find SageMaker features to improve the agility of your workflows, as well as the performance of your models. You'll be able to focus 100% on the machine learning problem at hand, without spending any time installing, managing, and scaling machine learning tools and infrastructure.

In this first chapter, we're going to learn what the main capabilities of SageMaker are, how they help solve pain points faced by machine learning practitioners, and how to set up SageMaker. This chapter will comprise the following topics:

• Exploring the capabilities of Amazon SageMaker
• Setting up Amazon SageMaker on your local machine
• Setting up Amazon SageMaker Studio
• Deploying one-click solutions and models with Amazon SageMaker JumpStart

Technical requirements

You will need an AWS account to run the examples included in this chapter. If you haven't got one already, please point your browser to https://aws.amazon.com/getting-started/ to learn about AWS and its core concepts, and to create an AWS account. You should also familiarize yourself with the AWS Free Tier (https://aws.amazon.com/free/), which lets you use many AWS services for free within certain usage limits.

You will need to install and configure the AWS CLI for your account (https://aws.amazon.com/cli/).

You will need a working Python 3.x environment. Installing the Anaconda distribution (https://www.anaconda.com/) is not mandatory but is strongly encouraged as it includes many projects that we will need (Jupyter, pandas, numpy, and more).

Code examples included in the book are available on GitHub at https://github.com/PacktPublishing/Learn-Amazon-SageMaker-second-edition. You will need to install a Git client to access them (https://git-scm.com/).

Exploring the capabilities of Amazon SageMaker

Amazon SageMaker was launched at AWS re:Invent 2017. Since then, a lot of new features have been added: you can see the full (and ever-growing) list at https://aws.amazon.com/about-aws/whats-new/machine-learning.

In this section, you'll learn about the main capabilities of Amazon SageMaker and its purpose. Don't worry, we'll dive deep into each of them in later chapters. We will also talk about the SageMaker Application Programming Interfaces (APIs), and the Software Development Kits (SDKs) that implement them.

The main capabilities of Amazon SageMaker

At the core of Amazon SageMaker is the ability to prepare, build, train, optimize, and deploy models on fully managed infrastructure at any scale. This lets you focus on studying and solving the machine learning problem at hand, instead of spending time and resources on building and managing infrastructure. Simply put, you can go from building to training to deploying more quickly. Let's zoom in on each step and highlight relevant SageMaker capabilities.

Preparing

Amazon SageMaker includes powerful tools to label and prepare datasets:

• Amazon SageMaker Ground Truth: Annotate datasets at any scale. Workflows for popular use cases are built in (image detection, entity extraction, and more), and you can implement your own. Annotation jobs can be distributed to workers that belong to private, third-party, or public workforces.
• Amazon SageMaker Processing: Run batch jobs for data processing (and other tasks such as model evaluation) using your own code written with scikit-learn or Spark.
• Amazon SageMaker Data Wrangler: Using a graphical interface, apply hundreds of built-in transforms (or your own) to tabular datasets, and export them in one click to a Jupyter notebook.
• Amazon SageMaker Feature Store: Store your engineered features offline in Amazon S3 to build datasets, or online to use them at prediction time.
• Amazon SageMaker Clarify: Using a variety of statistical metrics, analyze potential bias present in your datasets and models, and explain how your models predict.

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Table of contents

• Learn Amazon SageMaker Second Edition
• Contributors
• Preface
• Section 1: Introduction to Amazon SageMaker
• Chapter 1: Introducing Amazon SageMaker
• Chapter 2: Handling Data Preparation Techniques
• Section 2: Building and Training Models
• Chapter 3: AutoML with Amazon SageMaker Autopilot
• Chapter 4: Training Machine Learning Models
• Chapter 5: Training CV Models
• Chapter 6: Training Natural Language Processing Models
• Chapter 7: Extending Machine Learning Services Using Built-In Frameworks
• Chapter 8: Using Your Algorithms and Code
• Section 3: Diving Deeper into Training
• Chapter 9: Scaling Your Training Jobs
• Chapter 10: Advanced Training Techniques
• Section 4: Managing Models in Production
• Chapter 11: Deploying Machine Learning Models
• Chapter 12: Automating Machine Learning Workflows
• Chapter 13: Optimizing Prediction Cost and Performance
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