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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

Technical requirements

Throughout the book, all code examples will assume the use of Python 3.10.8 unless specified otherwise. Examples in this edition have been run on a 2022 Macbook Pro with an M2 Apple silicon chip, with Rosetta 2 installed to allow backward compatibility with Intel-based applications and packages. Most examples have also been tested on a Linux machine running Ubuntu 22.04 LTS. The required Python packages for each chapter are stored in conda environment .yml files in the appropriate chapter folder in the book’s Git repository. We will discuss package and environment management in detail later in the book. But in the meantime, assuming you have a GitHub account and have configured your environment to be able to pull and push from GitHub remote repositories, to get started you can clone the book repository from the command line:

git clone https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition.git

Assuming you have...

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