Write a multiple answer quizz question for this blog paragraph:
`ITEMS in Action
Once the model is trained, the query and product embeddings from ITEMS are informative representations that capture their semantic relevance. Evaluation with a manually labeled dataset shows that the embedding score between queries and products is well correlated with human perception of what’s relevant. Though the model was trained using historical conversion data, the embedding score is more stable and much less prone to popularity bias compared with the raw clickthrough rate. Training and deployment of ITEMS are facilitated by Instacart’s MLOps platform, Griffin. The model carries out daily offline scoring jobs that pre-computes the embeddings for new search queries and new products. The product embeddings are organized into indices using the FAISS library and served in an approximate nearest neighbor (ANN) service with daily updates to the indices, so that relevant products can be easily retrieved for incoming queries. For the query embeddings, we take a hybrid approach where the embedding for the vast majority (>95%) of the search queries are retrieved via FeatureStore lookup, and the few remaining ones not yet cached in FeatureStore are computed on the fly. This allows us to cover 100% of the search queries while maintaining a low latency (<8ms).` Write your response with exactly 4 paragraphs, separated with the markdown divider: ***.
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