In 2017, the emergence of foundation models and transfer learning marked a significant shift in machine learning practices. Traditionally, models were trained from scratch, requiring the initialization of parameters, often randomly. The introduction of foundation models, such as Google's BERT, demonstrated that many tasks involving text or image inputs share underlying similarities. For instance, object recognition tasks allow for the adaptation of pre-trained models to specific domains, such as classifying agricultural pests through fine-tuning. This process allows practitioners to leverage models trained on vast datasets, equipped with millions of parameters, providing a robust starting point for developing domain-specific applications. Consequently, organizations can benefit from enhanced performance and efficiency by building upon these large-scale pre-trained models rather than starting from zero.