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

Training at scale

When we introduced Ray in Chapter 6, Scaling Up, we mentioned use cases where the data or processing time requirements were such that using a very scalable parallel computing framework made sense. What was not made explicit is that sometimes these requirements come from the fact that we actually want to train many models, not just one model on a large amount of data or one model more quickly. This is what we will do here.

The retail forecasting example we described in Chapter 1, Introduction to ML Engineering uses a data set with several different retail stores in it. Rather than creating one model that could have a store number or identifier as a feature, a better strategy would perhaps be to train a forecasting model for each individual store. This is likely to give better accuracy as the features of the data at the store level which may give some predictive power will not be averaged out by the model looking at a combination of all the stores together. This...