Shaping a Machine Learning Team

“You may or may not remember this, but there once existed such a time that everyone talked about something completely different than the novel coronavirus, its impact on businesses everywhere and the global economy.

In this fabled period, eons ago, companies everywhere struggled to adapt to a new reality, a reality in which all that data they had collected during the “Big Data” phase of the industry would actually be used for something. Some of them are still working on this.

At Strongbytes we’ve worked on this problem for quite some time, helping our partners gather data scattered all around their infrastructure, analyze and display it using a variety of dashboards, and of course, use machine learning to forecast everything from the price of energy in a certain market, to how much money a company will have in the bank in six months’ time, to how many attendees a conference will actually get.

How did we do it? It all started with us having to answer a lot of questions about building a good team. Questions like what kind of a team? Which skills should they possess? Should it be a specialized machine learning team, or have machine learning specialists be part of larger teams? How should they collaborate with the other developers? Should they do Agile? What about DevOps?

As with all complex questions, the answer is invariably, “it depends”. It depends on the size of your company, your culture, your goals. Strongbytes is an Agile company by definition, centred around multiple cross-functional teams, with each team handling one or more projects by themselves. We don’t have specialized backend, frontend, and testing teams, but instead our teams each have people with backend, frontend, and testing skills. This setup has worked really well for us, and it makes the integration of machine learning specialists obvious – we should not have a machine learning team, but instead we should have machine learning specialists embedded in our existing teams.”

Continue reading on Strongbytes’ blog.