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

Chapter 5: Deployment Patterns and Tools

In this chapter, we will dive into some important concepts around the deployment of your Machine Learning (ML) solution. We will begin to close the circle of the ML development life cycle and lay the groundwork for getting your solutions out into the world.

The act of deploying software, of taking it from a demo you can show off to a few stakeholders to a service that will ultimately impact customers or colleagues, is a very exhilarating but often challenging exercise. It also remains one of the most difficult aspects of any ML project and getting it right can ultimately make the difference between generating value or just hype.

We are going to explore some of the main concepts that will help your ML engineering team cross the chasm between a fun proof-of-concept to solutions that can run on scalable infrastructure in an automated way.

In this chapter, we will cover the following topics:

  • Architecting systems
  • Exploring the...