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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

Chapter 7: Building Robust CI/CD Pipelines

In this chapter, you will learn about continuous operations in the MLOps pipeline. The principles you will learn in this chapter are key to driving continuous deployments in a business context. To get a comprehensive understanding and first-hand experience, we will go through the concepts and hands-on implementation simultaneously. We will set up a CI/CD pipeline for the test environment while learning about components of continuous integration (CI) and continuous deployment (CD), pipeline testing, and releases and types of triggers. This will equip you with the skills to automate the deployment pipelines of machine learning (ML) models for any given scenario on the cloud with continual learning abilities in tune with business. Let's start by looking at why we need CI/CD in MLOps after all. We will continue by exploring the other topics as follows:

  • Continuous integration, delivery, and deployment in MLOps
  • Setting up a CI/CD...
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