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.
Machine Learning for Beginners
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.