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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

Setting up our tools

To prepare for the work in the rest of this chapter, and indeed the rest of the book, it will be helpful to set up some tools. At a high level, we need the following:

  • Somewhere to code
  • Something to track our code changes
  • Something to help manage our tasks
  • Somewhere to provision infrastructure and deploy our solution

Let's look at how to approach each of these in turn:

  • Somewhere to code: First, although the weapon of choice for coding by data scientists is of course Jupyter Notebook (other solutions are available), once you begin to make the move toward ML engineering, it will be important to have an Integrated Development Environment (IDE) to hand. An IDE is basically an application that comes with a series of built-in tools and capabilities to help you to develop the best software that you can. PyCharm is an excellent example for Python developers and comes with a wide variety of plugins, add-ons, and integrations useful...
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