Assembling your team

There are no set rules about how you should pull together a team for your machine learning project, but there are some good general principles to follow, and gotchas to avoid.

First, always bear in mind that unicorns do not exist. 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 a bit 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.

Secondly, blended is best. We all know the benefits of diversity for organizations and teams in general and this should, of course, apply to your machine learning team as well. Within a project, you will need the mathematics, the code, the engineering, the project management, the communication, and a variety of other skills to succeed. So, given the previous point, make sure you cover this in at least some sense across your team.

Third, tie your team structure to your projects in a dynamic way. If you are working on a project that is mostly about getting the data in the right place and the actual machine learning models are really simple, focus your team profile on the engineering and data modeling aspects. If the project requires a detailed understanding of the model, and it is quite complex, then reposition your team to make sure this is covered. This is just sensible and frees up team members who would otherwise have been underutilized to work on other projects.

As an example, suppose that you have been tasked with building a system that classifies customer data as it comes into your shiny new data lake, and the decision has been taken that this should be done at the point of ingestion via a streaming application. The classification has already been built for another project. It is already clear that this solution will heavily involve the skills of the data engineer and the ML engineer, but not so much the data scientist since that portion of work has been completed in another project.

In the next section, we will look at some important points to consider when deploying your team on a real-world business problem.