In our previous blog, we outlined that Supervised Machine Learning (ML) models need labeled data, but majority of the data collected in the raw format lacks labels. So, the first step before building a ML model would be to get the raw data labeled by domain experts. To do so, we had outlined how Doccano is an easy tool for collaborative text annotation. However, not all data that gets collected is in text format, many a times we end up with a bunch of images but the end goal is again to build a Supervised ML model. Like stated previously, the first step would be to tag these images with specific labels.

This is a Part 2 of my Machine Learning in iOS tutorials, check Part 1 first. You may have read my previous article about Machine Learning in iOS: IBM Watson and CoreML. So you know that Machine Learning can be intimidating with lots of concepts and frameworks to learn, not to mention that we need to understand algorithms in Python. Talking about image labelling, you might have a bunch of images and you want to train the machine to understand and classify them. Training your own custom deep learning models can be challenging.