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

Concept to solution in four steps

All ML projects are unique in some way: the organization, the data, the people, and the tools and techniques employed will never be exactly the same for any two projects. This is good, as it signifies progress as well as the natural variety that makes this such a fun space to work in.

That said, no matter the details, broadly speaking, all successful ML projects actually have a good deal in common. They require translation of a business problem into a technical problem, a lot of research and understanding, proofs of concept, analyses, iterations, consolidation of work, construction of the final product, and deployment to an appropriate environment. That is ML engineering in a nutshell!

Developing this a bit further, you can start to bucket these activities into rough categories or stages, the results of each being necessary inputs for later stages. This is shown in Figure 2.6:

Figure 2.6 – The stages that any ML project...