As companies collect more and more data about their customers, an increased amount of duplicate information starts appearing in the data as well, causing a lot of confusion among internal teams. Since it would be impossible to manually go through all of the data and delete the duplicates, companies have come up with machine learning solutions that perform such work for them. Today we would like to take a look at some interesting uses of machine learning to catch duplicates in all kinds of environments. Before we dive right in, let’s take a look at how machine learning systems work.
How Do Machine Learning Systems Identify Duplicates?
When a person looks at an image or two strings of data it would be fairly easy for them to determine whether or not the images or strings are duplicates. However, how would you train a machine to spot such duplicates? Perhaps a good starting point would be to identify all of the similarities, but then you would need to explain exactly what 'similar' means. Are there gradations to similarities? In order to overcome such challenges, researchers use string metrics to train machine learning models.