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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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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

An implementation roadmap for your solution

Having a well-defined method and milestones ensures the successful delivery of the desired ML solution (using MLOps methods). In this section, we will discuss a generic implementation roadmap that can facilitate MLOps for any ML problem in detail. The goal of this roadmap is to solve the problem with the right solution:

Figure 2.10 – Implementation roadmap for an MLOps-based solution

Using the preceding roadmap, we can transition from ML development to MLOps with clear milestones, as shown in these three phases for MLOps implementation. Now, let's look into these three phases of the roadmap in more detail. It's worth noting that after the following section on theory, we will get into the practical implementation of the roadmap and work on a real-world business use case.

Phase 1 – ML development

This is the genesis of implementing the MLOps framework for your problem; before beginning...

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