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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

Demystifying SageMaker Studio notebooks, instances, and kernels

Figure 2.10 is an architectural diagram of the SageMaker Studio domain and how a notebook kernel relates to other components. There are four entities we need to understand here:

  • EC2 instance: The hardware that the notebook runs on. You can choose what instance type to use based on the vCPU, GPU, and amount of memory. The instance type determines the pricing rate, which can be found in https://aws.amazon.com/sagemaker/pricing/.
  • SageMaker image: A container image that can be run on SageMaker Studio. It contains language packages and other files required to run a notebook. You can run multiple images in an EC2 instance.
  • KernelGateway app: A SageMaker image runs as a KernelGateway app. There is a one-to-one relationship between a SageMaker image and a KernelGateway app.
  • Kernel: A process that runs the code in a notebook. There can be multiple kernels in a SageMaker image.

So far, we, as User1 in...

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