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)
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This chapter was all about building a solid foundation for future work. We discussed the development steps common to all ML engineering projects, which we called “Discover, Play, Develop, Deploy,” and contrasted this way of thinking against traditional methodologies like CRISP-DM. In particular, we outlined the aim of each of these steps and their desired outputs.

This was followed by a high-level discussion of tooling and a walkthrough of the main setup steps. We set up the tools for developing our code, keeping track of the changes to that code, managing our ML engineering project, and finally, deploying our solutions.

In the rest of the chapter, we went through the details for each of the four steps we outlined previously, with a particular focus on the Develop and Deploy stages. Our discussion covered everything from the pros and cons of Waterfall and Agile development methodologies to environment management and then software development best practices. We explored how to package your ML solution and what deployment infrastructure is available for you to use, and outlined the basics of setting up your DevOps and MLOps workflows. We finished up the chapter by discussing, in some detail, how to apply testing to our ML code, including how to automate this testing as part of CI/CD pipelines. This was then extended into the concepts of continuous model performance testing and continuous model training.

In the next chapter, we will turn our attention to how to build out the software for performing the automated training and retraining of your models using a lot of the techniques we have discussed here.

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