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Amazon SageMaker Best Practices

You're reading from   Amazon SageMaker Best Practices Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801070522
Length 348 pages
Edition 1st Edition
Languages
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Authors (3):
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Randy DeFauw Randy DeFauw
Author Profile Icon Randy DeFauw
Randy DeFauw
Shelbee Eigenbrode Shelbee Eigenbrode
Author Profile Icon Shelbee Eigenbrode
Shelbee Eigenbrode
Sireesha Muppala Sireesha Muppala
Author Profile Icon Sireesha Muppala
Sireesha Muppala
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Processing Data at Scale
2. Chapter 1: Amazon SageMaker Overview FREE CHAPTER 3. Chapter 2: Data Science Environments 4. Chapter 3: Data Labeling with Amazon SageMaker Ground Truth 5. Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing 6. Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store 7. Section 2: Model Training Challenges
8. Chapter 6: Training and Tuning at Scale 9. Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger 10. Section 3: Manage and Monitor Models
11. Chapter 8: Managing Models at Scale Using a Model Registry 12. Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants 13. Chapter 10: Optimizing Model Hosting and Inference Costs 14. Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify 15. Section 4: Automate and Operationalize Machine Learning
16. Chapter 12: Machine Learning Automated Workflows 17. Chapter 13:Well-Architected Machine Learning with Amazon SageMaker 18. Chapter 14: Managing SageMaker Features across Accounts 19. Other Books You May Enjoy

Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing

So far, we've identified our dataset and explored both manual and automated labeling. Now it's time to turn our attention to preparing the data for training. Data scientists are familiar with the steps of feature engineering, such as scaling numeric features, encoding categorical features, and dimensionality reduction.

As motivation, let's consider our weather dataset. What if our input dataset is imbalanced or not really representative of the data we'll encounter in production? Our model will not be as accurate as we'd like, and the consequences can be profound. Some facial recognition systems have been trained on datasets weighted toward white faces, with distressing consequences (https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/?web=1&wdLOR=cB09A9880-DF39-442C-A728-B00E70AF1CA9).

We need to understand what input...

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