The technology industry loves throwing around the term machine learning (ML). It's used in a variety of contexts, from technology providers claiming to have "invented the math" behind machine learning, to others applying it to less than scientific outcomes. This doesn't help the fact that the science of ML as it applies to cybersecurity is probably one of the most complex and least understood topics today. To bring some clarity to the topic, let's walk through five key steps you'll need to take to develop and operationalize a true ML system capable of predicting an outcome based on the data it trains on. "Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data."