The text discusses the challenges and advancements in few-shot learning, particularly in scenarios where large datasets are scarce, such as in healthcare. While traditional machine learning requires extensive data, few-shot learning enables models to learn from just a few labeled examples, facilitated by visual language models (VLMs) that leverage vast internet data. The University of Toronto Engineering Science students have collaborated to develop a production-ready few-shot learning system utilizing adapter methods, which efficiently fine-tune models with minimal computational cost. Their system, deployable using FastAPI, Redis, and Docker, supports up to 10 million class instances and showcases potential for scalability and adaptability. The team is exploring alternative base models, data augmentation techniques, and the integration of large language models to enhance few-shot learning capabilities.