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

Writing good Python

As discussed throughout this book, Python is an extremely popular and very versatile programming language. Some of the most widely used software products in the world, and some of the most widely used ML engineering solutions in the world, use Python as a core language. Given this scope and scale, it is clear that if we are to write similarly amazing pieces of ML-driven software, we should once again follow the best practices and standards already adopted by these solutions. In the following sections, we will explore what packaging up means in practice, and start to really level up our ML code in terms of quality and consistency.

Recapping the basics

Before we get stuck into some more advanced concepts, let's make sure we are all on the same page and go over some of the basic terminology of the Python world. This will ensure that we apply the right thought processes to the right things and that we can feel confident when writing our code.

In Python...