👉 Activated engineering refers to the process of converting raw, unprocessed data into a structured, usable format that can be effectively utilized by machines and software systems. This involves applying various techniques such as data cleaning, transformation, feature extraction, and normalization to enhance the quality and relevance of the data. By activating data through these methods, engineers and scientists can unlock deeper insights, improve model performance, and enable more accurate predictions or decisions. Activated engineering bridges the gap between raw data and actionable intelligence, making it a crucial step in the data science and machine learning pipeline.
