Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
2.5 (2)
Book Image

Machine Learning Engineering with Python - Second Edition

2.5 (2)
By: Andrew P. McMahon

Overview of this book

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Table of Contents (12 chapters)
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...