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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

Building your package

In our example, we can package up our solution using the setuptools library. In order to do this, you must create a file called setup.py that contains the important metadata for your solution, including the location of the relevant packages it requires. An example of setup.py is shown in the following code block. This shows how to do this for a simple package that wraps some of the outlier detection functionality we have been mentioning in this chapter:

from setuptools import setup
setup(name='outliers',
     version='0.1',
     description='A simple package to wrap some outlier detection functionality',
     author='Andrew McMahon',
     license='MIT',
     packages=['outliers'],
     zip_safe=False)

We can see that setuptools allows you to supply...

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