Data labelling -- overcoming AI projects' biggest obstacle

Building artificial intelligence (AI) models is not like building software. It requires a constant'test and learn' approach. Algorithms are continually learning and data is being refined -- and as much relevant, high-quality data as possible is key. Data labelling is an integral part of data pre-processing for machine learning. If you're training a system to identify animals in images, for example, you might provide it with thousands of images of various animals from which to learn the common features of each, which would eventually enable it to identify animals in unlabelled images.