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

Packaging your code

In some ways, it is interesting that Python has taken the world by storm. It is dynamically typed and non-compiled, so it can be quite different to work with compared to Java or C++. This particularly comes to the fore when we think about packaging our Python solutions. For a compiled language, the main target is to produce a compiled artifact that can run on the chosen environment, a Java jar for example. Python requires that the environment you run in has an appropriate Python interpreter and the ability to install the libraries and packages you need. There is also no single compiled artifact created, so you often need to deploy your whole code base as is.

Despite this, Python has indeed taken the world by storm, especially for ML. As we are ML engineers thinking about taking models to production, we would be remiss to not understand how to package and share Python code in a way that helps others to avoid repetition, to trust in the solution, and to be able...