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

Learning about learning

At their heart, ML algorithms all contain one key feature: an optimization of some kind. The fact that these algorithms learn (meaning that they iteratively improve their performance concerning an appropriate metric upon exposure to more observations) is what makes them so powerful and exciting. This process of learning is what we refer to when we say training.

In this section, we will cover the key concepts underpinning training, the options we can select in our code, and what these mean for the potential performance and capabilities of our training system.

Defining the target

We have just stated that training is an optimization, but what exactly are we optimizing? Let's consider supervised learning. In training, we provide the labels or values that we would want to predict for the given feature so that the algorithms can learn the relationship between the features and the target. To optimize the internal parameters of the algorithm during training, it needs...