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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

Ensemble Model Serving Pattern

In this chapter, we will discuss the ensemble model serving pattern. In the ensemble pattern, we combine the output from multiple models before serving a response to the client. This combination of responses from multiple sources is needed in many scenarios – for example, to get information about audio and video using separate models from a video file, and then combining that information to generate the final inference about the video. We can also combine the output from multiple similar models to make inferences with higher confidence. We will discuss some of these cases in this chapter. We will also explore a dummy end-to-end example of how we can combine multiple models to generate the final response.

At a high level, we are going to cover the following main topics in this chapter:

  • Introducing the ensemble pattern
  • Using ensemble pattern techniques
  • End-to-end dummy example of serving the model
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