👉 Avoiding computing is a strategy employed in machine learning and data processing to reduce computational costs and time by minimizing the amount of data processed or computations performed. This can be achieved through various techniques such as sampling, where only a subset of the full dataset is used for training or inference, or by employing dimensionality reduction methods that lower the complexity of the input data. Another approach involves using approximation algorithms or simpler models that require less computational power while still maintaining acceptable performance levels. Additionally, techniques like early stopping in training loops can prevent unnecessary computations when further iterations do not significantly improve model performance. Overall, avoiding computing aims to make data processing and model training more efficient and scalable, especially when dealing with large datasets or resource-constrained environments.
