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

Hosting your own microservice on AWS

A classic way to surface your ML models is via a lightweight web service hosted on a server. This can be a very flexible pattern of deployment. You can run a web service on any server with access to the internet (roughly) and, if designed well, it is often easy to add further functionality to your web service and expose it via new endpoints.

In Python, the two most used web frameworks have always been Django and Flask. In this section, we will focus on Flask as it is the simpler of the two and has been written about extensively for ML deployments on the web, so you will be able to find plenty of material to build on what you learn here.

On AWS, one of the simplest ways you can host your Flask web solution is as a containerized application on an appropriate platform. We will go through the basics of doing this here, but we will not spend time on the detailed aspects of maintaining good web security for your service. To fully discuss this may...