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

Choosing a style

This section will provide a summary of two coding styles or paradigms, which make use of different organizational principles and capabilities of Python. Whether you write your code in an object-orientated or functional style could just be an aesthetic choice. This choice, however, can also provide other benefits, such as code that is more aligned with the logical elements of your problem, code that is easier to understand, or even more performant code.

In the following sections, we will outline the main principles of each paradigm and allow you to choose for yourself based on your use case.

Object-oriented programming

Object-Oriented Programming (OOP) is a style where the code is organized around, you guessed it, abstract objects with relevant attributes and data instead of around the logical flow of your solution. The subject of OOP is worth a book (or several books!) in itself, so we will focus on the key points that are relevant for our ML engineering journey...