How Small Businesses Can Integrate Machine Learning Into Their Model Posted on : Dec 12 - 2017

Small-business owners are always on the hunt for opportunities that can give their businesses a leg up. In the past, small business owners have eagerly adopted software-as-a-service products, transitioned to cloud infrastructure and embraced self-service digital advertising. Today, one of the most exciting opportunities is the potential to leverage machine learning (ML) to give your business a competitive advantage. ML solutions automate workflows, enhance data-driven decisions and facilitate interactions with customers.

Two factors that are making ML accessible to small businesses are the commoditization of machine learning algorithms and the democratization of pre-trained ML models. These two shifts have allowed my team to help several small businesses build custom ML solutions that would have been pipe dreams even five years ago. We've built a range of solutions from automatically flagging fraudulent online survey submissions to a HIPAA compliant content recommendation engine. Throughout this process, I've realized that there are broad misunderstandings around what machine learning is and how businesses can effectively build ML solutions. So as a small business owner, how can you leverage these ML opportunities to benefit your business?

What Is Machine Learning?

Before delving into how SMBs can build machine learning solutions, it’ll be helpful to understand what ML really is. Contrary to corporate marketing, machine learning isn’t black magic. Instead, it's a class of algorithms that allow computers to perform pattern matching extremely efficiently. Many of these algorithms have been around since the 1970s. Newer ones have emerged from academia more recently. Interestingly, many of these algorithms aren’t particularly complicated to implement but they are computationally expensive. These computational costs are one of the factors that have made ML algorithms difficult to use in the past. So how do companies use ML today?

Let’s jump back to before machine learning and consider how your credit card company might have evaluated that a given transaction is fraudulent. They may have pulled your payment history, the details of your previous transactions and then evaluated the details of the potentially fraudulent one against this history. An analyst may have seen that you never missed a payment, had an average transaction size of $100 and only used your card at a handful of merchants. Then they could evaluate the potentially fraudulent transaction within these parameters, using their gut instinct decide that it was fraudulent. View More