The whole premise of machine learning is that you're using past data to make predictions about the future. So, if your past data doesn't mimic what's going to happen in the future–if in reality, there's a lot of outliers that are inconsistent with the prior data–then a machine is just not going to perform well in those situations. And that's when machine learning is not the right solution. Machine Learning shines when it's working with repeatable tasks.
Podcast
Demystifying Machine Learning
Demystifying the difference between artificial intelligence (AI) and machine learning (ML) is not easy - so many people use them interchangeably instead of seeking out the difference. We dive into this during this episode and explore some of the many applications of machine learning that propel vastly different industries. We also discuss the recent advancements in ML that are generating more interest than ever before. And finally, since no technology comes without challenges, our experts discuss recommendations on ways to combat potential complications so that you can win in curves in this critical time of innovation.
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Episode Highlights
Finding Success With Your Business Problem
For Machine Learning to be successful, you first need to ensure you've very clearly defined the business problem it's solving for. Too often, people create amazing statistical models that yield amazing insights, but ultimately can't be used because it doesn't answer the true business problem. It would be better to start small and address the most pressing problem than to get overly excited and waste time building something really cool that you can't actually use.
Tom Estella
Managing Director
Tom is a managing director in CapTech's Atlanta office and leads our firm's machine learning initiatives. He has deep experience scoping, measuring, and creating Advanced Analytical solutions
Gabriella Baum
Managing Director
