Throughout the book, all code examples will assume the use of Python 3.10.8 unless specified otherwise. Examples in this edition have been run on a 2022 Macbook Pro with an M2 Apple silicon chip, with Rosetta 2 installed to allow backward compatibility with Intel-based applications and packages. Most examples have also been tested on a Linux machine running Ubuntu 22.04 LTS. The required Python packages for each chapter are stored in conda environment .yml files in the appropriate chapter folder in the book’s Git repository. We will discuss package and environment management in detail later in the book. But in the meantime, assuming you have a GitHub account and have configured your environment to be able to pull and push from GitHub remote repositories, to get started you can clone the book repository from the command line:

git clone https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition.git

Assuming you have...

The explosion of data and the potential applications of it over the past few years have led to a proliferation of job roles and responsibilities. The debate that once raged over how a data scientist was different from a statistician has now become extremely complex. I would argue, however, that it does not have to be so complicated. The activities that have to be undertaken to get value from data are pretty consistent, no matter what business vertical you are in, so it should be reasonable to expect that the skills and roles you need to perform these steps will also be relatively consistent. In this chapter, we will explore some of the main data disciplines that I think you will always need in any data project. As you can guess, given the name of this book, I will be particularly keen to explore the notion of ML engineering and how this fits into the mix.

Let’s now look at some of the roles involved in using data in the modern landscape...

In modern software organizations, there are many different methodologies to organize teams and get them to work effectively together. We will cover some of the project management methodologies that are relevant in Chapter 2, The Machine Learning Development Process, but in the meantime, this section will discuss some important points you should consider if you are ever involved in forming a team, or even if you just work as part of a team, that will help you become an effective teammate or lead.

First, always bear in mind that nobody can do everything. You can find some very talented people out there, but do not ever think one person can do everything you will need to the level you require. This is not just unrealistic; it is bad practice and will negatively impact the quality of your products. Even when you are severely resource-constrained, the key is for your team members to have a laser-like focus to succeed.

Second, blended is best. We all...

The majority of us who work in ML, analytics, and related disciplines do so for organizations with a variety of different structures and motives. These could be for for-profit corporations, not-for-profits, charities, or public sector organizations like government or universities. In pretty much all of these cases, we do not do this work in a vacuum and not with an infinite budget of time or resources. It is important, therefore, that we consider some of the important aspects of doing this type of work in the real world.

First of all, the ultimate goal of your work is to generate value. This can be calculated and defined in a variety of ways, but fundamentally your work has to improve something for the company or its customers in a way that justifies the investment put in. This is why most companies will not be happy for you to take a year to play with new tools and then generate nothing concrete to show for it, or to spend your days only reading...

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...

When you get down to the nuts and bolts of building your solution, there are so many options for tools, tech, and approaches that it can be very easy to be overwhelmed. However, as alluded to in the previous sections, a lot of this complexity can be abstracted to understand the bigger picture via some back-of-the-envelope architecture and designs. This is always a useful exercise once you know what problem you will try and solve, and it is something I recommend doing before you make any detailed choices about implementation.

To give you an idea of how this works in practice, what follows are a few worked-through examples where a team has to create a high-level ML systems design for some typical business problems. These problems are similar to ones I have encountered before and will likely be similar to ones you will encounter in your own work.

Example 1: Batch anomaly detection service

You work for a tech-savvy taxi ride company with a fleet...

In this chapter, we introduced the idea of ML engineering and how that fits within a modern team building valuable solutions based on data. There was a discussion of how the focus of ML engineering is complementary to the strengths of data science and data engineering and where these disciplines overlap. Some comments were made about how to use this information to assemble an appropriately resourced team for your projects.

The challenges of building ML products in modern real-world organizations were then discussed, along with pointers to help you overcome some of these challenges. In particular, the notions of reasonably estimating value and effectively communicating with your stakeholders were emphasized.

This chapter then rounded off with a taster of the technical content to come in later chapters, through a discussion of what typical ML solutions look like and how they should be designed (at a high level) for some common use cases.

These topics are important...