How to Build an Optimal Machine Learning Team Posted on : Nov 13 - 2019

An effective ML team is constantly evolving based on many different factors. Assess your specific needs and use cases before putting a team into action.

Machine learning solutions and workflows are meant to save time and vastly improve operational efficiency, but you still need the right human team to ensure every aspect is optimized and running on all cylinders.

Before getting started with finding the right people, you should take stock of the business problem at hand. The goal of an ML initiative may be to optimize rote business processes (e.g. automation) or it may be to establish a core piece business offering. No matter the case, it is imperative to first establish how the ML model fits within the greater workflow. Once your organization understands the implications of ML on the business, then it can begin to assemble the optimal team.

In an ideal world, what does that team look like? What mix of talent is needed to optimize results? The short answer is that it can vary based on your needs in a given instance. That said, in most cases, you won’t need to hire a full-stack ML team. On the most basic level, you should have one data scientist on board in an advisory role, whether it’s as a consultant or a member of the board, to help the engineering team figure out what it needs to do as challenges arise.

Starting with scientists and engineers, let’s take a closer look at the roles that every ML team is likely to utilize at one point or another:

Data scientists and ML engineers

Data scientists are the team members that might be able to create new architectures such as neural networks to solve the business problem because they’re constantly up to date on the latest technology. Their role is to guide the team on the state-of-the-art and how to structure the problem to achieve the appropriate business metrics. Scientists also often help engineers troubleshoot the ML model -- the papers they’ve read and conferences they’ve attended will likely inform them on which variations you can accomplish higher accuracy (or other metrics) with a specific model.

ML engineers are the folks who know which type of architecture they need to build and train the ML model. They should be well-versed in trying out different data, saving the model, and figuring out how to get the model into production. In addition, engineers help make sure the entire pipeline can support rapid development and iterative increments after launch. In short, model management is the responsibility of the engineering team.

For medium-sized companies, having more engineers than scientists is most often the way to go. However, if a workflow is at the proof-of-concept level, having more scientists available is preferable because they know how to solve the technological problems involved. If the said problem is already solved (for example, a large body of scientific literature or frameworks already exist), then multiple scientists likely won’t be necessary. View More