A quick look at ML

First, let’s understand what ML is from a data engineering perspective. ML is a data process that uses data as input. The output of the process is a generalized formula for one specific objective, which is called the ML model.

As an illustration, let’s imagine some of the real-world use cases that use ML. The first example is a recommendation system from an eCommerce platform. This eCommerce platform may use ML to use the customer’s purchase history as input data. This data can be processed to calculate how likely each customer will purchase other items in the future. Another example is a cancer predictor that uses X-ray images from the health industry. A collection of X-ray images with cancer and without cancer can be used as input data and be used to predict unidentified X-ray images.

I believe you’ve heard about those kinds of ML use cases and many other real-world use cases. Even in the latest hype surrounding generative AI,...