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Should see a rise in embedded KV popularity in correlation with ML applications. Storing embeddings in something like leveldb in formats such as flatbuffer offe
by ramoz 4y ago
Should see a rise in embedded KV popularity in correlation with ML applications. Storing embeddings in something like leveldb in formats such as flatbuffer offer high-performance solutions for online prediction (i.e. for mapping business values to their embedding format on the fly to send off to some model for inference).
- jupp0r 4y agoWould that be on mobile devices for offline usage? I'm thinking that for typical backend use cases one would use a dedicated key value store service, right?
- ramoz 4y agoThis would depend on your requirements and type of inference. Say you need to compute inference across 1000's of content/documents/images every second or so, out of some corpus of millions-billions, then having a kv store on disk/SSD (NVME) might be for more efficient & cheaper (in terms of grabbing those embeddings to conduct a downstream ML task). How you update the corpus matters too -- a lot of embedding spaces need to be updated in aggregate.
- porker 4y agoI've heard this a lot recently about storing embeddings. As someone who has dabbled in ML I don't understand what it means. Can you point me to a good overview of the topic please?