Vector databases are a relatively new way for interacting with abstract data representations derived from opaque machine learning models such as deep learning architectures. These representations are often called vectors or embeddings and they are a compressed version of the data used to train a machine learning model to accomplish a task like sentiment analysis, speech recognition, object detection, and many others.
These new databases shine in many applications like semantic search and recommendation systems, and here, we’ll learn about one of the most popular and fastest growing vector databases in the market, Qdrant.
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