Pinecone has upgraded its vector database, aiming at enterprises that are looking to boost productivity in machine learning projects.
Built by the team behind Amazon Sagemaker, Pinecone is designed to allow machine learning engineers to search through catalogues of embeddings, the continuous vector representations of separate variables fundamental to common ML algorithms such as word2vec.
With its 2.0 iteration, the company promises storage of metadata – such as a topic, author, and category – with each item, allowing users to filter vector searches by these criteria in a single stage.
Edo Liberty, founder and CEO of Pinecone, said that while relational databases use SQL to organise and query data, and text documents require an index, machine learning models relate to meanings or sentiment represented in multidimensional vectors.
"You don't care about the specific words; you care about meanings and sentiment. You need to do that with AI, and the way ...
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