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

Summary

In this chapter, we learned about the important topic of how to build up our solutions for training and staging the ML models that we want to run in production. We split the components of such a solution into pieces that tackled training the models, the persistence of the models, serving the models, and triggering retraining for the models. I termed this the “Model Factory.”

We got into the more technical details of some important concepts with a deep dive into what training an ML model really means, which we framed as learning about how ML models learn. Some time was then spent on the key concepts of feature engineering, or how you transform your data into something that a ML model can understand during this process. This was followed by sections on how to think about the different modes your training system can run in, which I termed “train-persist” and “train-run.”

We then discussed how you can perform drift detection on...