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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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "To use Amazon SageMaker Debugger, you must enhance Estimator with three additional configuration parameters: DebuggerHookConfig, Rules, and ProfilerConfig."

A block of code is set as follows:

#Feature group name
weather_feature_group_name_offline = 'weather-feature-group-offline' + strftime('%d-%H-%M-%S', gmtime())

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

@smp.step
def train_step(model, data, target):
       output = model(data)
       long_target = target.long()
       loss = F.nll_loss(output, long_target, reduction="mean")
       model.backward(loss)
       return output, loss
    return output, loss 

Any command-line input or output is written as follows:

$ mkdir css
$ cd css

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Keep in mind that when you use multiple instances in the training cluster, all instances should be in the same Availability Zone."

Tips or important notes

Appear like this.

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