This video explores different machine learning search strategies in Amazon OpenSearch Service, including sparse retrieval, dense retrieval, and hybrid search. The presenter demonstrates a web application that showcases keyword search, vector search, hybrid search, and multimodal search using a retail dataset. Viewers learn how to improve search relevance using various techniques like sparse embeddings, vector similarity, and combining multiple search methods. The demo highlights the use of remote machine learning models from Amazon SageMaker and Amazon Bedrock to generate embeddings for text and images. By the end, viewers understand how to build and fine-tune ML-powered search solutions using OpenSearch Service, adapting to different business use cases.