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  • Book Overview & Buying Machine Learning Engineering with Python
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Machine Learning Engineering with Python

Machine Learning Engineering with Python

By : Andrew P. McMahon
4.9 (21)
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Machine Learning Engineering with Python

Machine Learning Engineering with Python

4.9 (21)
By: Andrew P. McMahon

Overview of this book

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.
Table of Contents (13 chapters)
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1
Section 1: What Is ML Engineering?
4
Section 2: ML Development and Deployment
9
Section 3: End-to-End Examples

Summary

This chapter has been all about best practices for when you write your own Python packages for your ML solutions. We went over some of the basic concepts of Python programming as a refresher before covering some tips and tricks and good techniques to bear in mind. We covered the importance of coding standards in Python and PySpark. We then performed a comparison between object-oriented and functional programming paradigms for writing your code. We moved onto the details of taking the high-quality code you have written and packaging it up into something you can distribute across multiple platforms and use cases. To do this, we looked into different tools, designs, and setups you could use to make this a reality. This included a brief discussion of how to find good use cases for packaging up. We continued with a summary of some housekeeping tips for your code, including how to test, log, and monitor in your solution. We finished with a brief philosophical point on the importance...

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Tech Concepts
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Programming languages
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Machine Learning Engineering with Python
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