Video: AI Playbook with Eric Siegel: who do you need on AI project team?
Speakers: Eric Siegel, Steven Ramirez
Description:
Author Eric Siegel, and Beyond the Arc’s Steven Ramirez, discuss Siegel’s latest work, The AI Playbook. They shed light on an often-overlooked aspect of AI and machine learning implementation: the critical role of the team. Siegel emphasizes the indispensable duo at the forefront: the data scientist and the business professional.
See more on our blog: beyondthearc.com/blog
Transcript
So Eric, I know we’re gonna get a chance to talk about the book in more detail next week. But I am curious when you talk about it being a playbook, you know, to me that immediately brings to mind that there’s a team, right? So there’s a team that’s responsible for the successful deployment of machine learning and ai. If that’s the case, who’s on, who do you need on this team and what are some of the roles of the people who are playing on this team?
Well, you need, first and foremost, you need the business person and the data person, right? The data professional and the business professional, the data scientist and their client, the person who’s gonna do the number crunching, who’s gonna run machine learning methods or software on the historical data, and the business side person in charge of, let’s say, managing the operations that are meant to, to be improved with the predictions that are output by the predictive model that’s generated by machine learning, right? So those are maybe the first two individuals.
That isn’t to say necessarily which one of the two is quote, leading the project, and I’m actually agnostic about that, depends on the organization and how the project evolved. But you also typically need data engineers to set up the right pipelines to prepare the data appropriately for training. You need potentially analytics translators. There’s a whole bunch of rules.
And yes, it’s definitely a team sport. But first and foremost, my, my message today is to bridge the gap, which tends to be wide between the people with who have more of a data background and those with more of a business background. Both of them are needed more than anything.
If you’re a business stakeholder, your participation after ramping up with some basic semi-technical understanding, not the rocket science, but how to make use of the rocket science is absolutely the most important thing needed to improve the success rate of machine learning projects.