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

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

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Table of Contents (18) Chapters

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

Structuring your MLOps

The primary goal of MLOps is to make an organization or set of individuals collaborate efficiently to build data and ML-driven assets to solve their business problems. As a result, overall performance and transparency are increased. Working in silos or developing functionalities repeatedly can be extremely costly and time-consuming.

In this section, we will explore how MLOps can be structured within organizations. Getting the MLOps process right is of prime importance. By selecting the right process and tools for your MLOps, you and your team are all set to implement a robust, scalable, frugal, and sustainable MLOps process. For example, I recently helped one of my clients in the healthcare industry to build and optimize their MLOps, which resulted in 76% cost optimization (for storage and compute resources) compared to their previous traditional operations.

The client's team of data scientists witnessed having 30% of their time freed up from mundane...