Book Image

Machine Learning Engineering with Python

By : Andrew P. McMahon
Book Image

Machine Learning Engineering with Python

By: Andrew P. McMahon

Overview of this book

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.
Table of Contents (13 chapters)
1
Section 1: What Is ML Engineering?
4
Section 2: ML Development and Deployment
9
Section 3: End-to-End Examples

Section 3: End-to-End Examples

This section has the objective of working through some concrete examples in a way that pulls together all of the previous discussions in the book. These examples can be used as templates by readers of the book for future applications in their own work. The examples will have a particular focus on what decisions need to be made to select the best engineering approach, given what has been learned in the rest of the book. Once you have completed this section, you will have seen two examples of how to apply your new knowledge to archetypal machine learning engineering projects.

This section comprises the following chapters:

  • Chapter 7, Building an Example ML Microservice
  • Chapter 8, Building an Extract Transform Machine Learning Use Case