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This is the sequel talk to the earlier OpenSearchCon talk on the topic “Budget Friendly Semantic Search With Neural Sparse Search”. This follow up talk we will explain shortcomings such as 1/ pre-trained lack the domain knowledge 2/ off-the-shelf-domain-specific-sparse-model are either expensive/proprietary or non-existent
Level 300-400 talk covering, 1/ Intro to Neural Sparse Search 2/ Pre-trained model shortcomings 3/ Fine-tuning pre-trained model using open-source tools 4/ Use Search Relevancy Workbench to compare pre-trained and fine-tuned sparse model search results
A recommended pre-requisite is to watch the earlier presentation on “Budget Friendly Semantic Search With Neural Sparse Search” - https://www.youtube.com/watch?v=kx71KFf-Nv0
Of course its a completely “optional” pre-requisite as the previous talk will be condensed to a 5 minute lightning talk and will serve as the introduction
This talk will be demo-heavy, showcasing the fine-tuning possibilities of sparse encoder models using data from an existing OpenSearch index, which also means that the approach is suitable for even for those niche domains for which even proprietary models are not available in the market
Aswath Srinivasan is a Senior Search Engine Architect at Amazon Web Services currently based in Munich, Germany. With over 18 years of experience in various search technologies, Aswath currently focuses on OpenSearch. He is a search and open-source enthusiast and helps customers and... Read More →
Principal Specialist Solutions Architect, Amazon Web Services
Cédric Pelvet is a Principal Specialist Solutions Architect at AWS, focusing on AI and near-realtime distributed systems for data like OpenSearch, Kafka and Flink.
Friday April 17, 2026 13:35 - 14:15 CEST Bohemia 2
