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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
Languages
Tools
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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

Technical requirements

As in the other chapters, to create the environment to run the code examples in this chapter you can run:

conda env create –f mlewp-chapter09.yml

This will include installs of Airflow, PySpark, and some supporting packages. For the Airflow examples, we can just work locally, and assume that if you want to deploy to the cloud, you can follow the details given in Chapter 5, Deployment Patterns and Tools. If you have run the above conda command then you will have installed Airflow locally, along with PySpark and the Airflow PySpark connector package, so you can run Airflow as standalone with the following command in the terminal:

airflow standalone

This will then instantiate a local database and all relevant Airflow components. There will be a lot of output to the terminal, but near the end of the first phase of output, you should be able to spot details about the local server that is running, including a generated user ID and password...

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