👉 Front engineering in the context of machine learning and neural networks refers to the process of modifying or adapting existing models, architectures, or training procedures to improve their performance, efficiency, or suitability for specific tasks without fundamentally altering their underlying structure. This can involve tweaking hyperparameters, adjusting the network's topology, incorporating regularization techniques to prevent overfitting, or integrating auxiliary tasks to enhance learning. The goal is to optimize the model's capabilities for particular applications, such as faster inference times, better generalization on unseen data, or improved accuracy on specific datasets, while maintaining or even reducing computational costs. Front engineering is a crucial step in the model development pipeline, allowing practitioners to fine-tune models to achieve state-of-the-art performance in practical scenarios.
