👉 PB (Pre-trained) Engineering in the context of machine learning involves the systematic process of adapting pre-trained models, typically large language or vision models, to specific tasks or domains with limited labeled data. This process leverages the rich knowledge and representations learned from vast, diverse datasets to enhance model performance on downstream tasks. Key steps include fine-tuning the pre-trained model on task-specific datasets, adjusting hyperparameters, and often incorporating techniques like transfer learning or domain adaptation. The goal is to achieve high accuracy and efficiency, reducing the need for extensive labeled data and computational resources while effectively tailoring the model to meet the unique requirements of a particular application.
