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

Executing the build

As discussed in Chapter 2, The Machine Learning Development Process, there are several stages we have to go through on the ML project life cycle after performing discovery and building an initial proof-of-concept. These steps are focused on the development of the solution and then the deployment of that solution.

First, we will focus on how we would break down these stages into manageable tasks that could be executed by our engineering team. Each component in Figure 7.2 roughly corresponds to one of these tasks, as follows:

  • Prediction Handler / Training Handler: Each of these will consist of application logic that takes a request from the dashboard (via an API request over HTTP) and then triggers the appropriate process. These can be brought together as different endpoints in a simple web service that acts as the interface between the dashboard and the other components of the system.
  • Training Pipeline and Forecaster: As discussed in the previous section...