You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801070157

Length 326 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Michael Hsieh

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio

2. Chapter 1: Machine Learning and Its Life Cycle in the Cloud FREE CHAPTER

3. Chapter 2: Introducing Amazon SageMaker Studio

4. Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio

5. Chapter 3: Data Preparation with SageMaker Data Wrangler

6. Chapter 4: Building a Feature Repository with SageMaker Feature Store

7. Chapter 5: Building and Training ML Models with SageMaker Studio IDE

8. Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify

9. Chapter 7: Hosting ML Models in the Cloud: Best Practices

10. Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot

11. Part 3 – The Production and Operation of Machine Learning with SageMaker Studio

12. Chapter 9: Training ML Models at Scale in SageMaker Studio

13. Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor

14. Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry

15. Other Books You May Enjoy

Chapter 2: Introducing Amazon SageMaker Studio

As we just learned in Chapter 1, Machine Learning and Its Life Cycle in the Cloud, an ML life cycle is complex and iterative. Steps can be quite manual even though most things are done with coding. Having the right tool for an ML project is essential for you to be successful in delivering ML models for production in the cloud. With this chapter, you are in the right place! Amazon SageMaker Studio is a purpose-built ML Integrated Development Environment (IDE) that offers features covering an end-to-end ML life cycle to make developers' and data scientists' jobs easy in the AWS Cloud.

In this chapter, we will cover the following:

Introducing SageMaker Studio and its components
Setting up SageMaker Studio
Walking through the SageMaker Studio UI
Demystifying SageMaker Studio notebooks, instances, and kernels
Using the SageMaker Python SDK

The rest of the chapter is locked

You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Chapter 2: Introducing Amazon SageMaker Studio

Authors (1)

Personalised recommendations for you

You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Chapter 2: Introducing Amazon SageMaker Studio

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you