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

Going deep with deep learning

In this book, we have worked with relatively “classical” ML models so far, which rely on a variety of different mathematical and statistical approaches to learn from data. These algorithms in general are not modeled on any biological theory of learning and are at their heart motivated by finding procedures to explicitly optimize the loss function in different ways. A slightly different approach that the reader will likely be aware of, and that we met briefly in the section on Learning about learning in Chapter 3, From Model to Model Factory, 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 or nodes (modeled on biological neurons), we can build systems that can effectively model any mathematical function (see the information box below...