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
