Learn Amazon SageMaker

A guide to building, training, and deploying machine learning models for developers and data scientists

Julien Simon

• Publisher: Packt Publishing
• Year: 2020
• ISBN:
  9781800203594
• Edition: 1
• Topic: Computer Science
• Subtopic: Entreprise Applications
• Index: Computer Science

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor

Key Features

• Build, train, and deploy machine learning models quickly using Amazon SageMaker
• Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques
• Improve productivity by training and fine-tuning machine learning models in production

Book Description

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.

What you will learn

• Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS)
• 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 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 tools

Who this book is for

This 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. Some 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, Getting Started with Amazon SageMaker
• Chapter 2, Handling Data Preparation Techniques

Chapter 1: Introduction to 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 ML models. Whether you're just beginning with ML 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 ML problem at hand, without spending any time installing, managing, and scaling ML 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 ML practitioners, and how to set up SageMaker:

• 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

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 Command-line Interface (CLI) for your account (https://aws.amazon.com/cli/).

You will need a working Python 3.x environment. Be careful not to use Python 2.7, as it is no longer maintained. 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. 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 their purpose. Don't worry, we'll dive deep on 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 build, train, optimize, and deploy models on fully managed infrastructure, and at any scale. This lets you focus on studying and solving the ML 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.

Building

Amazon SageMaker provides you with two development environments:

• Notebook instances: Fully managed Amazon EC2 instances that come preinstalled with the most popular tools and libraries: Jupyter, Anaconda, and so on.
• Amazon SageMaker Studio: A full-fledged integrated development environment for ML projects.

When it comes to experimenting with algorithms, you can choose from the following:

• A collection of 17 built-in algorithms for ML and deep learning, already implemented and optimized to run efficiently on AWS. No ML code to write!
• A collection of built-in open source frameworks (TensorFlow, PyTorch, Apache MXNet, scikit-learn, and more), where you simply bring your own code.
• Your own code running in your own container: custom Python, R, C++, Java, and so on.
• Algorithms and pretrained models from AWS Marketplace for ML (https://aws.amazon.com/marketplace/solutions/machine-learning).

In addition, Amazon SageMaker Autopilot uses AutoML to automatically build, train, and optimize models without the need to write a single line of ML code.

Amazon SageMaker also includes two major capabilities that help with building and preparing 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 data processing and model evaluation batch jobs, using either scikit-learn or Spark.

Training

As mentioned earlier, Amazon SageMaker takes care of provisioning and managing your training infrastructure. You'll never spend any time managing servers, and you'll be able to focus on ML. On top of this, SageMaker brings advanced capabi...