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

Summary

In this chapter, we have walked through an example of how to take the tools and techniques from the first six chapters of this book and apply them together to solve a realistic business problem. We have discussed in detail how the need for a dynamically triggered forecasting algorithm can lead very quickly to a design that requires several small services to interact seamlessly. In particular, we created a design with components responsible for handling events, training models, storing models, and performing predictions. We then walked through how we would choose our toolset to build to this design in a real-world scenario, by considering things such as appropriateness for the task at hand as well as likely developer familiarity. Finally, we carefully defined the key pieces of code that would be required to build the solution in a way that could solve the problem repeatedly and robustly.

In the next, and final, chapter, we will build out an example of a batch ML process....