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

Engineering features for machine learning

Before we feed any data into an ML model, it has to be transformed into a state that can be understood by our models. We also need to make sure we only do this on the data we deem useful for improving the performance of the model, as it is far too easy to explode the number of features and fall victim to the curse of dimensionality. This refers to a series of related observations where, in high-dimensional problems, data becomes increasingly sparse in the feature space, so achieving statistical significance can require exponentially more data. In this section, we will not cover the theoretical basis of feature engineering. Instead, we will focus on how we, as ML engineers, can help automate some of the steps in production. To this end, we will quickly recap the main types of feature preparation and feature engineering steps so that we have the necessary pieces to add to our pipelines later in this chapter.

Engineering categorical features...