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 discussed some of the most important concepts when it comes to deploying your ML solutions. In particular, we focused on the concepts of architecture and what tools we could potentially use when deploying solutions to the cloud. We covered some of the most important patterns used in modern ML engineering and how these can be implemented with tools such as containers and AWS Elastic Container Registry and Elastic Container Service, as well as how to create scheduled pipelines in AWS using Managed Workflows for Apache Airflow. We also explored how to hook up the MWAA example with GitHub Actions, so that changes to your code can directly trigger updates of running services, providing a template to use in future CI/CD processes.

In the next chapter, we will look at the question of how to scale up our solutions so that we can deal with large volumes of data and high throughput calculations.