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

Spinning up serverless infrastructure

Whenever we do any ML or software engineering, we have to run the requisite tasks and computations on computers, often with appropriate networking, security, and other protocols and software already in place, which we have often referred to already as constituting our infrastructure. A big part of our infrastructure is the servers we use to run the actual computations. This might seem a bit strange, so let's start by talking about serverless infrastructure (how can there be such a thing?). This section will explain this concept and show you how to use it to scale out your ML solutions.

Serverless is a bit misleading as a term as it does not mean that no physical servers are running your programs. It does mean, however, that the programs you are running should not be thought of as being statically hosted on one machine, but as ephemeral instances on another layer on top of the underlying hardware.

The benefits of serverless tools for...