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

Containerizing

If you develop software that you want to deploy somewhere, which is the core aim of an ML engineer, then you have to be very aware of the environmental requirements of your code, and how different environments might affect the ability of your solution to run. This is particularly important for Python, which does not have a core capability for exporting programs as standalone executables (although there are options for doing this). This means that Python code needs a Python interpreter to run and needs to exist in a general Python environment where the relevant libraries and supporting packages have been installed.

A great way to avoid headaches from this point of view is to ask the question: Why can't I just put everything I need into something that is relatively isolated from the host environment, which I can ship and then run as a standalone application or program? The answer to this question is that you can and that you do this through containerization. This...