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

Testing, logging, and error handling

Building code that performs an ML task may seem like the end goal, but it is only one piece of the puzzle. We also want to be confident that this code will work and if it doesn't, we will be able to fix it. This is where the concepts of testing, logging, and error handling come in, which the next few sections cover at a high level.

Testing

One of the most important features that sets your ML engineered code apart from typical research scripts is the presence of robust testing. It is critical that any system you are designing for deployment can be trusted not to fall down all the time and that you can catch issues during the development process.

Luckily, since Python is a general-purpose programming language, it is replete with tools for performing tests on your software. In this chapter, we will use PyTest, which is one of the most popular, powerful, and easy-to-use testing toolsets for Python code available. PyTest is particularly...