Dealing with the Lack of Data in Machine Learning

In many projects I carried out, companies, despite having fantastic AI business ideas, display a tendency to slowly become frustrated when they realize that they do not have enough data… However, solutions do exist! The purpose of this article is to briefly introduce you to some of them (the ones that are proven effective in my practice) rather than to list all existing solutions.

The problem of data scarcity is very important since data are at the core of any AI project. The size of a dataset is often responsible for poor performances in ML projects.

Most of the time, data related issues are the main reason why great AI projects cannot be accomplished. In some projects, you come to the conclusion that there is no relevant data or the collection process is too difficult and time-consuming.

Supervised machine learning models are being successfully used to respond to a whole range of business challenges. However, these models are data-hungry and their performance relies heavily on the size of training data available. In many cases, it is difficult to create training datasets that are large enough.

Another issue I could mention is that project analysts tend to underestimate the amount of data necessary to handle common business problems. I remember myself struggling to collect big…