Improve action house search with vector capabilities: Bedrock or SageMaker Serverless inference.

Search engines guide users from their initial goals to the information that fulfills those goals. The retrieved items are deemed relevant when they align with the user’s intent. Ensuring relevant results involves having the data in the catalog, analyzing it accurately, representing it through text and vectors, ranking it appropriately, and performing other actions. In this session, discover how auction houses utilize vector search, powered by Amazon OpenSearch Service, to find similar items within their extensive database, enhancing the customer experience and preventing issues with counterfeit artworks, and see how it assists users in obtaining the most relevant results. This session also uncovers some peculiarities in getting embeddings with AWS Bedrock and AWS SageMaker serverless inference for the abovementioned goals.