Explaining Machine Learning with Facets

Lack of the right data often poisons an artificial intelligence (AI) project from the start. We are used to downloading ready-to-use datasets from Kaggle, scikit-learn, and other reliable sources.

We focus on learning how to use and implement machine learning (ML) algorithms. However, reality hits AI project managers hard on day one of a project.

Companies rarely have clean or even sufficient data for a project. Corporations have massive amounts of data, but they often come from different departments.

Each department of a company may have its own data management system and policy. When finally you obtain a training dataset sample, you may find that your AI model does not work as planned. You might have to change ML models or find out what is wrong with the data. You are trapped right from the start. What you thought would be an excellent AI project has turned into a nightmare.

You need to get out of this trap rapidly...