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
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11
Index

Retraining required

You wouldn’t expect that after finishing your education, you never read a paper or book or speak to anyone again, which would mean you wouldn’t be able to make informed decisions about what is happening in the world. So, you shouldn’t expect an ML model to be trained once and then be performant forever afterward.

This idea is intuitive, but it represents a formal problem for ML models known as drift. Drift is a term that covers a variety of reasons for your model’s performance dropping over time. It can be split into two main types:

  • Concept drift: This happens when there is a change in the fundamental relationship between the features of your data and the outcome you are trying to predict. Sometimes, this is also known as covariate drift. An example could be that at the time of training, you only have a subsample of data that seems to show a linear relationship between the features and your outcome. If it turns out that...