Cloud-native vector storage
Fully-managed Vector Storage for Machine Learning Applications
Nnext.net provides a managed service that lets you search for points in a vector space and find the 'nearest neighbors' for those points by Euclidean distance or cosine similarity.Get Started
Simplify and speed up your machine-learning applications.
As a data scientist or machine learning engineer, you think in vectors and embeddings. Now your datastore does too.
Search and retrieval
Searching for similar or relevant items is one of the most important and widely used scenarios for nearest neighbor search. In a search and retrieval application, your system should be able to convert the implicit or explicit query to an embedding, compare that embedding to the embedding of the stored items, and retrieve the most similar ones. Example applications include: retrieving the most relevant news articles for a given search query; retrieving the pictures that are most similar to a user-provided picture.
After your entities have been encoded in real-valued feature vectors and stored in nnext, you can perform exploratory analytics and visualization to discover interesting information and patterns. This can include: finding the nearest items to a given one; Identifying groups of similar items in the embedding space; finding the density of items in a particular item's neighborhood; Identifying boundary or intergroup items as well as exceptional or outlier items.
Utilized in a variety of areas, recommendation systems, are most commonly recognized as playlist generators for video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter. Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also known as the personality-based approach), as well as other systems such as knowledge-based systems.
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