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

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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

Developing and collaborating using SageMaker Notebook

The SageMaker Studio IDE makes collaboration and customization easy. Besides the freedom of choosing the kernel and instance backing a SageMaker notebook, you could also manage Git repositories, compare notebooks, and share notebooks.

Users can interact with a Git repository easily in SageMaker Studio, and you may have already done so to clone the sample repository from GitHub for this book. Not only can you clone a repository from a system terminal, you can also use the Git integration in the left sidebar in the UI to graphically interact with your code base, as shown in Figure 5.4. You can conduct actions you would normally do in Git with the UI: switching branches, pull, commit, and push.

Figure 5.4 – Graphical interface of Git integration in the SageMaker Studio IDE

You can also perform notebook diff on a changed file by right-clicking on the changed file and selecting Diff, as shown in Figure...

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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

Developing and collaborating using SageMaker Notebook

Authors (1)

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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

Developing and collaborating using SageMaker Notebook

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Authors (1)

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