When building machine learning systems for ranking and search relevance, it's crucial to measure the quality of the results for ongoing improvement of your models. Furthermore, you have to be able to identify edge cases in which your models are wrong, as well as detect corrupt data before it's exposed to users.

 

In this session, we'll show how you can track search results quality on architectures such as Elasticsearch and vector databases like Pinecone.