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Machine Learning Model Serving Patterns and Best Practices

You're reading from   Machine Learning Model Serving Patterns and Best Practices A definitive guide to deploying, monitoring, and providing accessibility to ML models in production

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803249902
Length 336 pages
Edition 1st Edition
Languages
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Author (1):
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Md Johirul Islam Md Johirul Islam
Author Profile Icon Md Johirul Islam
Md Johirul Islam
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1:Introduction to Model Serving
2. Chapter 1: Introducing Model Serving FREE CHAPTER 3. Chapter 2: Introducing Model Serving Patterns 4. Part 2:Patterns and Best Practices of Model Serving
5. Chapter 3: Stateless Model Serving 6. Chapter 4: Continuous Model Evaluation 7. Chapter 5: Keyed Prediction 8. Chapter 6: Batch Model Serving 9. Chapter 7: Online Learning Model Serving 10. Chapter 8: Two-Phase Model Serving 11. Chapter 9: Pipeline Pattern Model Serving 12. Chapter 10: Ensemble Model Serving Pattern 13. Chapter 11: Business Logic Pattern 14. Part 3:Introduction to Tools for Model Serving
15. Chapter 12: Exploring TensorFlow Serving 16. Chapter 13: Using Ray Serve 17. Chapter 14: Using BentoML 18. Part 4:Exploring Cloud Solutions
19. Chapter 15: Serving ML Models using a Fully Managed AWS Sagemaker Cloud Solution 20. Index 21. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

4XX errors 78-80

reference link 81

5XX errors 81, 82

A

accuracy 85-87

advanced model configurations

using 248-252

aggregation 222, 223

majority class, selecting 223, 224

Amazon AWS monitoring

reference link 75

Amazon SageMaker 291, 292

benefits 292

features 292-294

notebook, creating 295-301

used, for serving model 295-304

Amazon SageMaker Studio Lab 293

Amazon SageMaker Training Compiler 293

Apache Airflow 202, 204

installing 202

used, for creating pipeline 204-211

used, for demonstrating machine learning pipeline 211-215

API Gateway 114

automatic periodic triggers 141

advantages 143

cron jobs 141

disadvantages 144

exercises 142, 143

B

batch model serving 29, 134

automatic periodic triggers 141...

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