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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

Hands-on deployment and inference testing (a business use case)

When you have your service (either API or ML) ready and you are about to serve it to the users but you don't have any clue about how many users it can actually handle and how it will react when many users access it simultaneously, that's where load testing is useful to benchmark how many users your service can serve and to validate whether the service can cater to the business requirements.

We will perform load testing for the service we deployed previously (in Chapter 7, Building Robust CI and CD Pipelines). Locust.io will be used for load testing. locust.io is an open source load-testing tool. For this, we will install locust (using pip) and curate a Python script using the locust.io SDK to test an endpoint. Let's get started by installing locust:

  1. Install locust: Go to your terminal and execute the following command:
    pip install locust

    Using pip, locust will be installed – it takes around...

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