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

View More author details

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

ML in research versus production

ML in research is implemented with specific goals and priorities to improve the state of the art in the field, whereas the aim of ML in production is to optimize, automate, or augment a scenario or a business.

In order to understand the deployment of ML models, let's start by comparing how ML is implemented in research versus production (in the industry). Multiple factors, such as performance, priority, data, fairness, and interpretability, as listed in Table 6.1, depict how deployments and ML work differently in research and production:

Table 6.1 – ML in research and production

Data

In general, data in research projects is static because data scientists or statisticians are working on a set dataset and trying to beat the current state-of-the-art models. For example, recently, many breakthroughs in natural language processing models have been witnessed, for instance, with BERT from Google or XLNet from Baidu...