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Pardon my ignorance but could you explain further how this might simplify things? It doesn’t seem like a ML-specific DB would make things easier on folks who don’t know ML in the first place.

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If you don't know ML and you want to do similarity search, you can just use the models that come together with EuclidesDB and just make calls to add items and query for similarity with less than 10 lines of Python code. So it will really simplify things for people who don't want to retrain any model or implement a backend, indexing, search, etc.

Hopefully I'm thinking this the right way:

If I'm searching for a particular breed of dog, and let's say EuclidesDB has already been trained. I just need to query based on my image of the dog breed?

How does it differ from me interacting directly with a model already saved after training on Tensorflow?

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