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

Going Deep

In this chapter, we have so far been working with relatively ‘classical’ machine learning models, which rely on a variety of different approaches to learn from data often motivated by mathematical arguments from researchers. These algorithms in general are not modelled on any biological theory of learning and are at their heart motivated by the statistics and mathematics. A slightly different approach that the reader will likely be aware of, and that we have met briefly in the sections on Learning About Learning, is that taken by Artificial Neural Networks (ANNs), which originated in the 1950s and were based on idealized models of neuronal activity in the brain. The core concept of an ANN is that through connecting relatively simple computational units called neurons (modelled on biological neurons) we can build systems that can effectively model any mathematical function. The neuron in this case is a small component of the system which will return a 0 or 1 result...