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

Designing our forecasting service

The requirements in the Understanding the forecasting problem section are the definitions of the targets we need to hit, but they are not the method for getting there. Drawing on our understanding of design and architecture from Chapter 5, Deployment Patterns and Tools, we can start building out our design.

First, we should confirm what kind of design we should be working to. Since we need dynamic requests, it makes sense that we follow the microservice architecture discussed in Chapter 5, Deployment Patterns and Tools. This will allow us to build a service that has the sole focus of retrieving the right model from our model store and performing the requested inference. The prediction service should therefore have interfaces available between the dashboard and the model store.

Furthermore, since a user may want to work with a few different store combinations in any one session and maybe switch back and forward between the forecasts of these,...