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

Not reinventing the wheel

You will already have noticed through this chapter (or I hope you have!) that a lot of the functionality that you need for your ML and Python project has already been built. One of the most important things you can learn as an ML engineer is that you are not supposed to build everything from scratch. You can do this in a variety of ways, the most obvious of which is to use other packages in your own solution and then build functionality that enriches what is already there. As an example, you do not need to build basic regression modeling capabilities since they exist in a variety of packages, but you might have to add a new type of regressor or use some specific domain knowledge or trick you have developed. In this case, you would be justified in writing your own code on top of the existing solution. You can also use a variety of concepts from Python, such as wrapper classes or decorators, as well. The key message is that although there is a lot of work for...