Data Acquisition comes after problem scoping and involves gathering the data that the AI will learn from. AI systems rely entirely on data to find patterns and make predictions, so this step is crucial. The data must be accurate, relevant, sufficient, and representative of the real-world situation. It can come from multiple sources such as sensors, databases, surveys, historical records, or online platforms. Poor-quality or insufficient data can lead to unreliable AI models.
In the school example, data acquisition would include collecting attendance records, homework submissions, and past exam scores for students. The more comprehensive and accurate the data, the better the AI model will perform.