Vector Databases for AI & ML

Vector Databases for AI & ML | Slides

Vector databases are a type of database designed to handle high-dimensional data, particularly in the field of AI and machine learning. They allow for efficient storage, processing, and computation on data represented as vectors. These databases are crucial in performing operations such as similarity search and nearest neighbor search on high-dimensional data, providing faster and more efficient processing.


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Vector Databases	Importance	Usage
Vector databases are a type of database that store, process, and perform computations on data in the form of vectors. They are designed to handle high-dimensional data and are particularly useful in the field of machine learning and artificial intelligence, where data is often represented as high-dimensional vectors. Vector databases can perform operations such as similarity search and nearest neighbor search efficiently.	With the rise of AI and machine learning, the need for efficient handling and processing of high-dimensional data has increased. Traditional databases are not designed to handle this type of data efficiently, leading to the emergence of vector databases. They allow for faster and more efficient processing of high-dimensional data, making them crucial in AI and machine learning applications.	Vector databases are used when dealing with high-dimensional data, particularly in the field of AI and machine learning. They are used when operations such as similarity search and nearest neighbor search need to be performed on the data. They are also used when the data needs to be processed and analyzed quickly and efficiently.

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