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

Chapter 2: The Machine Learning Development Process

In this chapter, we will define how the work for any successful Machine Learning (ML) software engineering project can be divided up. Basically, we will answer the question of how do you actually organize the doing of a successful ML project? We will not only discuss the process and workflow, but we will also set up the tools you will need for each stage of the process and highlight some important best practices with real ML code examples.

Specifically, this chapter will cover the concept of a discover, play, develop, deploy workflow for your ML projects, appropriate development tooling and their configuration and integration for a successful project. We will also cover version control strategies and their basic implementation, setting up Continuous Integration/Continuous Deployment (CI/CD) for your ML project. We will also introduce some potential execution environments. At the end of this chapter, you will be set up for success...