If your organization is new to Machine Learning (ML), there are some best practices and common pitfalls about which to be aware. Often, organizations become interested in the promise of ML or they want to play catch-up with their competitors and they decide it’s time to adopt ML too. But it’s not something that happens just because you want it. Often, leaders don’t know what problems ML can solve for or how to make the right business case to reach great results. To be successful, you must first build a strong, core team comprised of the right, cross-functional talent. In this podcast, Vinnie Schoenfelder, Tom Estella, and Ramon Matos dive in on the details of making your new ML initiatives yield powerful results.
ML can make your organization more efficient, reduce cost, and increase revenue. If you haven’t developed ML solutions yet, you might want to dig deeper—but what’s the first thing you need to do? Before marching forward with solution discussions, organizations first need to identify the business problem they’re solving for, why, and what their desired outcome is. ML for the sake of ML doesn’t yield valuable results. It’s important to identify the business problems that are a good fit for ML and go from there.
Build The Right Team
ML, in many ways, is no different than other successful and unsuccessful projects. In addition to a strong business case, the outcome will be as good as the team you build. Having data scientists on the team is important, but without the right partnership and collaboration, they can only go so far. To start, organizations need to make sure they have a strong, cross-functional team with a high degree of collaboration, including subject matter experts, leadership, and representatives from the areas of the business that will be impacted by the solution.
Vinnie is a Principal at CapTech and plays a large role in helping define services,
forge partnerships, and lead innovation for our clients. As a thought leader, he
regularly helps clients solve their most complex business challenges.