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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

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 metadata such as the name of the package, the version number, and the software license. Once you have this file in the root directory of your project, you can...

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