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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
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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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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

Summary

In this chapter, we have learned about the patterns in model serving. We have learned that patterns in model serving can be seen from two angles at a high level: serving patterns based on serving philosophies and serving patterns based on serving strategies.

Serving patterns based on serving philosophies involve the best practices in serving models. These patterns help us ensure resilient model serving by ensuring fault-tolerant, scalable processes in model serving.

Serving patterns based on serving strategies involve recurring approaches used for serving models for different business use cases – for example, a batch serving strategy if the predictions are not necessary immediately and online serving if the predictions are needed immediately.

We also discussed a high-level overview of each of the patterns. We saw that the serving principles such as stateless serving, continued model evaluation, and keyed prediction can help the uninterrupted and resilient serving...

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