👉 Representations engineering is the process of transforming raw data into structured, meaningful representations that capture essential features and relationships, enabling effective machine learning and AI applications. This involves selecting appropriate data formats, applying dimensionality reduction techniques, encoding categorical variables, and constructing feature vectors that encapsulate the underlying patterns and semantics of the data. By doing so, representations engineering bridges the gap between raw input and high-level understanding, allowing models to learn more efficiently and accurately. It is crucial in domains ranging from natural language processing and computer vision to recommendation systems, as it enhances the performance of models by providing them with a richer, more interpretable input.
