Few-shot and zero-shot learning are innovative machine learning paradigms that address the challenge of learning from limited labeled data, with few-shot learning using a small number of labeled examples for new classes and zero-shot learning classifying without any labeled examples by leveraging class descriptions. These techniques are beneficial in various fields, such as machine learning engineering, data analysis, data marketing, and labeling, by enabling efficient classification and identification of rare or challenging data points. The tutorial emphasizes the use of OpenAI's CLIP embeddings for image classification within the Labelbox Catalog, showcasing how these methods can be applied across different industries for data categorization. The workflow includes setting up classifiers, utilizing natural language and similarity search, and incorporating a human-in-the-loop system to enhance classification accuracy, demonstrating the practical application of these learning methods for automating data curation and improving data pipeline efficiency.