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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

SageMaker JumpStart model zoo

There are more than 200 popular prebuilt and pretrained models in SageMaker JumpStart for you to use out of the box or continue to train for your use case. What are they good for? Training an accurate deep learning model is time consuming and complex, even with the most powerful GPU machine. It also requires large amounts of training and labeled data. Now, with these models that have been developed by the community, pretrained on large datasets, you do not have to reinvent the wheel.

Model collection

There are two groups of models: text models and vision models in SageMaker JumpStart model zoo. These models are the most popular ones among the ML community. You can quickly browse the models in SageMaker JumpStart and select the one that meets your needs. On each model page, you will see an introduction to the model, its usage, and how to prepare a dataset for fine-tuning purposes. You can deploy models into AWS as a hosted endpoint for your use case...

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