Galileo x Zilliz: The Power of Vector Embeddings

Unstructured data is estimated to reach over 175 zettabytes by 2025, with 80% of it being unstructured. Vector embeddings are a numerical representation of complex data such as images and text, allowing for efficient comparison and storage. These embeddings can be extracted from trained machine-learning models, typically using the output of the second-to-last layer of a neural network. The size of the embeddings, training data quality, and model architecture are key factors to consider when generating vector embeddings.

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