Vector databases for machine learning
Practical AI
Vectors unify all kinds of data
Machine learning has historically relied on crafting features manually represented as vectors, like the ratio between volume and price or the time spent on a website. With the rise of deep learning, embedding has become a popular technique to automatically generate rich features for various types of data, including user behavior and network analysis. This approach enables data structures to handle not only structured data like tables but also unstructured data like audio, video, and free-flowing text. By utilizing embeddings, unstructured data can be featurized into high-dimensional vectors, providing a common framework to work with diverse data types and making them more manageable and usable.
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