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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 2: Characterizing Your Machine Learning Problem

In this chapter, you will get a fundamental understanding of the various types of Machine Learning (ML) solutions that can be built for production, and will learn to categorize the relevant operations in line with the business and technological needs of your organization. You will learn how to curate an implementation roadmap for operationalizing ML solutions, followed by procuring the necessary tools and infrastructure for any given problem. By the end of this chapter, you will have a solid understanding of how to architect robust and scalable ML solutions and procure the required data and tools for implementing these solutions.

ML Operations (MLOps) aims to bridge academia and industry using state-of-the-art engineering principles, and we will explore different elements from both industry and academia to get a holistic understanding and awareness of the possibilities. Before beginning to craft your MLOps solution, it is important...

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