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 8: Building an Extract Transform Machine Learning Use Case

Similar to Chapter 7, Building an Example ML Microservice, the aim of this chapter will be to try to crystallize a lot of the tools and techniques we have learned about throughout this book and apply them to a realistic scenario. This will be based on another use case introduced in Chapter 1, Introduction to ML Engineering, where we imagined the need to cluster taxi-ride data on a scheduled basis. We will explore this scenario so that we can outline the key decisions we would make if building a solution in reality, as well as discussing how to implement it by leveraging what has been covered in other chapters.

This use case will allow us to explore what is perhaps the most used pattern in Machine Learning (ML) solutions across the world—that of the batch inference process. Due to the nature of retrieving, transforming, and then performing ML on data, I have termed this Extract Transform Machine Learning ...