👉 Stop engineering is a software development practice where developers intentionally introduce artificial constraints or "stops" into the code to guide and optimize the learning process of machine learning models. These stops can take various forms, such as limiting the model's complexity, enforcing certain constraints on its architecture, or introducing penalties for undesirable behaviors. By doing so, stop engineering helps prevent overfitting, encourages the model to generalize better to unseen data, and can lead to more efficient and effective learning. This technique is particularly useful in scenarios where the model's capacity is high relative to the amount of training data, or when dealing with complex tasks where manual tuning of hyperparameters is impractical.
