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Machine Learning Engineering with Python

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

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801079259
Length 276 pages
Edition 1st 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 (13) Chapters Close

Preface 1. Section 1: What Is ML Engineering?
2. Chapter 1: Introduction to ML Engineering FREE CHAPTER 3. Chapter 2: The Machine Learning Development Process 4. Section 2: ML Development and Deployment
5. Chapter 3: From Model to Model Factory 6. Chapter 4: Packaging Up 7. Chapter 5: Deployment Patterns and Tools 8. Chapter 6: Scaling Up 9. Section 3: End-to-End Examples
10. Chapter 7: Building an Example ML Microservice 11. Chapter 8: Building an Extract Transform Machine Learning Use Case 12. Other Books You May Enjoy

Writing good Python

As discussed throughout this book, Python is an extremely popular and very versatile programming language. Some of the most widely used software products in the world, and some of the most widely used ML engineering solutions in the world, use Python as a core language. Given this scope and scale, it is clear that if we are to write similarly amazing pieces of ML-driven software, we should once again follow the best practices and standards already adopted by these solutions. In the following sections, we will explore what packaging up means in practice, and start to really level up our ML code in terms of quality and consistency.

Recapping the basics

Before we get stuck into some more advanced concepts, let's make sure we are all on the same page and go over some of the basic terminology of the Python world. This will ensure that we apply the right thought processes to the right things and that we can feel confident when writing our code.

In Python...

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