Data storage

So far, we’ve used CSV files and Excel files to store our data. It’s an easy way to work with ML, but it is also a local one. However, when we want to scale our application and use it outside of just our machine, it is often much more convenient to use a real database engine. The database plays a crucial role in an ML pipeline by providing a structured and organized repository for storing, managing, and retrieving data. As ML applications increasingly rely on large volumes of data, integrating a database into the pipeline becomes essential for a few reasons.

Databases offer a systematic way to store vast amounts of data, making it easily accessible and retrievable. Raw data, cleaned datasets, feature vectors, and other relevant information can be efficiently stored in the database, enabling seamless access by various components of the ML pipeline.

In many ML projects, data preprocessing is a critical step that involves cleaning, transforming, and aggregating...