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Machine Learning Engineering  with Python

You're reading from   Machine Learning Engineering with Python Manage the lifecycle of machine learning models using MLOps with practical examples

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Length 462 pages
Edition 2nd Edition
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Author (1):
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Andrew P. McMahon Andrew P. McMahon
Author Profile Icon Andrew P. McMahon
Andrew P. McMahon
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Table of Contents (12) Chapters Close

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process FREE CHAPTER 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case 10. Other Books You May Enjoy
11. Index

Choosing a style

This section will provide a summary of two coding styles or paradigms, which make use of different organizational principles and capabilities of Python. Whether you write your code in an object-orientated or functional style could just be an aesthetic choice. This choice, however, can also provide other benefits, such as code that is more aligned with the logical elements of your problem, code that is easier to understand, or even more performant code.

In the following sections, we will outline the main principles of each paradigm and allow you to choose for yourself based on your use case.

Object-oriented programming

Object-Oriented Programming (OOP) is a style where the code is organized around, you guessed it, abstract objects with relevant attributes and data instead of around the logical flow of your solution. The subject of OOP is worth a book (or several books!) in itself, so we will focus on the key points that are relevant for our ML engineering journey.

First...

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