What does an ML solution look like?

When you think of ML engineering, you would be forgiven for defaulting to imagining working on voice assistance and visual recognition apps (I fell into this trap in previous pages, did you notice?). The power of ML, however, lies in the fact that wherever there is data and an appropriate problem, it can help and be integral to the solution.

Some examples might help make this clearer. When you type a text message and your phone suggests the next words, it can very often be using a natural language model under the hood. When you scroll any social media feed or watch a streaming service, recommendation algorithms are working double time. If you take a car journey and an app forecasts when you are likely to arrive at your destination, there is going to be some kind of regression at work. Your loan application often results in your characteristics and application details being passed through a classifier. These applications are not the ones shouted about on the news (perhaps with the exception of when they go horribly wrong), but they are all examples of brilliantly put-together ML engineering.

In this book, the examples we work through will be more like these; typical scenarios for machine learning encountered in products and businesses every day. These are solutions that, if you can build them confidently, will make you an asset to any organization.

We should start by considering the broad elements that should constitute any ML solution, as indicated in the following diagram:

Figure 1.3 – Schematic of the general components or layers of any ML solution and what they are responsible for

Your storage layer constitutes the endpoint of the data engineering process and the beginning of the ML one. It includes your data for training, your results from running your models, your artifacts, and important metadata. We can also consider this as including your stored code.

The compute layer is where the magic happens and where most of the focus of this book will be. It is where training, testing, prediction, and transformation all (mostly) happen. This book is all about making this layer as well-engineered as possible and interfacing with the other layers. You can blow this layer up to incorporate these pieces as in the following workflow:

Figure 1.4 – The key elements of the compute layer

Important note

The details are discussed later in the book, but this highlights the fact that at a fundamental level, your compute processes for any ML solution are really just about taking some data in and pushing some data out.

The surfacing layer is where you share your ML solution's results with other systems. This could be through anything from application database insertion to API endpoints, to message queues, to visualization tools. This is the layer through which your customer eventually gets to use the results, so you must engineer your system to provide clean and understandable outputs, something we will discuss later.

And that is it in a nutshell. We will go into detail about all of these layers and points later, but for now, just remember these broad concepts and you will start to understand how all the detailed technical pieces fit together.

Why Python?

Before moving on to more detailed topics, it is important to discuss why Python has been selected as the programming language for this book. Everything that follows that pertains to higher-level topics such as architecture and system design can be applied to solutions using any or multiple languages, but Python has been singled out here for a few reasons.

Python is colloquially known as the lingua franca of data. It is a non-compiled, not strongly typed, and multi-paradigm programming language that has clear and simple syntax. Its tooling ecosystem is also extensive, especially in the analytics and machine learning space. Packages such as scikit-learn, numpy, scipy, and a host of others form the backbone of a huge amount of technical and scientific development across the world. Almost every major new software library for use in the data world has a Python API. It is the third most popular programming language in the world, according to the TIOBE index (https://www.tiobe.com/tiobe-index/) at the time of writing (January 2021).

Given this, being able to build your systems using Python means you will be able to leverage all of the excellent machine learning and data science tools available in this ecosystem, while also ensuring that you build applications that can play nicely with other software.