What is the first step in the machine learning (ML) pipeline?

The first step in the machine learning pipeline is data collection. This stage is critical because the quality and quantity of data directly affect the performance of machine learning models. Successful ML systems rely on diverse and comprehensive datasets that accurately represent the problem domain. In this phase, data is gathered from various sources, which may include databases, online repositories, or sensors, depending on the application's context.

Collecting the right data is essential to ensure that the model can learn effectively; if the data is insufficient or flawed, it can lead to poor predictions and insights in later stages. Subsequently, the other steps in the pipeline, such as data analysis, data modeling, and data visualization, heavily depend on the foundation laid during the data collection phase. Effective data collection sets the stage for successful data management, analysis, and the eventual development of machine learning algorithms.