👉 Update computing, also known as continuous learning or online learning, refers to a machine learning approach where models are updated incrementally as new data becomes available, rather than being retrained from scratch on the entire dataset. This method allows models to adapt and improve over time by incorporating new information while retaining previously learned knowledge, making them particularly useful for dynamic environments where data evolves continuously. Unlike traditional batch learning, which requires reprocessing all historical data for model updates, update computing enables real-time or near-real-time model refinement, enhancing the model's ability to generalize and maintain performance in changing conditions. This approach is crucial for applications like fraud detection, recommendation systems, and autonomous vehicles, where timely adaptation to new patterns or anomalies is essential.
