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Engineering MLOps

You're reading from   Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800562882
Length 370 pages
Edition 1st Edition
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Author (1):
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Emmanuel Raj Emmanuel Raj
Author Profile Icon Emmanuel Raj
Emmanuel Raj
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Framework for Building Machine Learning Models
2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER 3. Chapter 2: Characterizing Your Machine Learning Problem 4. Chapter 3: Code Meets Data 5. Chapter 4: Machine Learning Pipelines 6. Chapter 5: Model Evaluation and Packaging 7. Section 2: Deploying Machine Learning Models at Scale
8. Chapter 6: Key Principles for Deploying Your ML System 9. Chapter 7: Building Robust CI/CD Pipelines 10. Chapter 8: APIs and Microservice Management 11. Chapter 9: Testing and Securing Your ML Solution 12. Chapter 10: Essentials of Production Release 13. Section 3: Monitoring Machine Learning Models in Production
14. Chapter 11: Key Principles for Monitoring Your ML System 15. Chapter 12: Model Serving and Monitoring 16. Chapter 13: Governing the ML System for Continual Learning 17. Other Books You May Enjoy

Production testing methods

As there are various businesses in operation, so are different types of production systems serving these businesses. In this section, we look into the different types of production systems or setups commonly used and how to test them.

Batch testing

Batch testing validates your model by performing testing in an environment that is different from its training environment. Batch testing is carried out on a set of samples of data to test model inference using metrics of choice, such as accuracy, RMSE, or f1-score. Batch testing can be done in various types of computes, for example, in the cloud, or on a remote server or a test server. The model is usually served as a serialized file, and the file is loaded as an object and inferred on test data.

A/B testing

You will surely have come across A/B testing. It is often used in service design (websites, mobile apps, and so on) and for assessing marketing campaigns. For instance, it is used to evaluate...

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