To make decisions more quickly and accurately, enterprises are increasingly turning to machine learning, arguably today’s most practical application of AI. Machine learning systems apply algorithms to data to glean insights into that data without explicit programming: It’s about using data to answer questions. As such, companies are applying machine learning to a wide array of issues, from customer purchasing patterns to predictive maintenance.
But before a machine learning system can answer questions, it must first be trained on data and outcomes. That’s because, while not explicitly programmed, machine learning systems need to develop and hone their ability to make predictions from data through experience with the same kind of data it will use to answer questions. For example, to predict whether a component is about to fail, a machine learning system must first be trained to do so by being fed sets of sensor readings from both functional and failing components.
This apparently prosaic stage between choosing your machine learning algorithm and deploying your data model is actually a key step in getting machine learning right: Get it wrong and you’ll end up with a system that doesn’t deliver what you want. There are some common mistakes that often happen when training machine learning systems; there are also decisions that need to be made early on, long before a machine learning system is deployed, that will be challenging and costly to address later.
Here’s what to look out for.