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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

Use cases for online model serving

Online model serving is essential when we need to see the impact of recent data on the model as soon as possible instead of waiting for a periodic batch update. In this section, we will discuss the following example cases where online serving is needed:

  • Recommending the nearest emergency center during a pandemic
  • Predicting the favorite soccer team in a tournament
  • Predicting the path of a hurricane or storm
  • Predicting the estimated delivery time of delivery trucks

Case 1 – recommending the nearest emergency center during a pandemic

Let’s assume a lot of people are becoming sick due to a pandemic, such as Covid, every day. However, there is a limited number of hospitals. We want to recommend the nearest, most convenient hospital to a patient, keeping the following things in mind:

  • The distance between the patient and the hospital should be short
  • The number of patients sent to a particular hospital...
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