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
