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Getting Started with Amazon SageMaker Studio

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

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801070157
Length 326 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Hsieh Michael Hsieh
Author Profile Icon Michael Hsieh
Michael Hsieh
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Table of Contents (16) Chapters Close

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

Walking through the SageMaker Studio UI

Figure 2.6 is a screenshot of the SageMaker Studio UI and the Studio Launcher page. You may find the interface very similar to the JupyterLab interface. SageMaker Studio indeed builds on top of JupyterLab and adds many additional features to it to provide you with an end-to-end ML experience within the IDE:

Figure 2.6 – The SageMaker Studio UI – the left sidebar is indicated in the red box

Let's talk about the key components in the Studio UI.

The main work area

The main work area is where the Launcher page, the notebooks, code editor, terminals, and consoles go. In addition to these base features from JupyterLab, as you will learn throughout the book, SageMaker Studio's own features, such as Data Wrangler, Autopilot, JumpStart, Feature Store, Pipelines, Model Monitor, and Experiments, also deliver the rich user experience in the main work area. The Launcher page is the portal to all the...

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