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Learn Amazon SageMaker

You're reading from   Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801817950
Length 554 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Julien Simon Julien Simon
Author Profile Icon Julien Simon
Julien Simon
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Amazon SageMaker
2. Chapter 1: Introducing Amazon SageMaker FREE CHAPTER 3. Chapter 2: Handling Data Preparation Techniques 4. Section 2: Building and Training Models
5. Chapter 3: AutoML with Amazon SageMaker Autopilot 6. Chapter 4: Training Machine Learning Models 7. Chapter 5: Training CV Models 8. Chapter 6: Training Natural Language Processing Models 9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks 10. Chapter 8: Using Your Algorithms and Code 11. Section 3: Diving Deeper into Training
12. Chapter 9: Scaling Your Training Jobs 13. Chapter 10: Advanced Training Techniques 14. Section 4: Managing Models in Production
15. Chapter 11: Deploying Machine Learning Models 16. Chapter 12: Automating Machine Learning Workflows 17. Chapter 13: Optimizing Prediction Cost and Performance 18. 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: "You can use the describe-spot-price-history API to collect this information programmatically."

A block of code is set as follows:

od = sagemaker.estimator.Estimator(     container,     role,     train_instance_count=2,                                      train_instance_type='ml.p3.2xlarge',                                      train_use_spot_instances=True,     train_max_run=3600,                     # 1 hours      train_max_wait=7200,                    # 2 hour      output_path=s3_output)

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

[<sagemaker.model_monitor.model_monitoring.MonitoringExecution at 0x7fdd1d55a6d8>,<sagemaker.model_monitor.model_monitoring.MonitoringExecution at 0x7fdd1d581630>,<sagemaker.model_monitor.model_monitoring.MonitoringExecution at 0x7fdce4b1c860>]

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "We can find more information about our monitoring job in the SageMaker console, in the Processing jobs section."

Tips or important notes

Appear like this.

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