![]() ![]() Such purpose-built vector databases should be designed to easily incorporate new indexes for emerging application scenarios and support flexible scale-out to multiple nodes to accommodate ever-growing data volumes. Businesses benefit from purpose-built, open source vector databases that have matured to the point where they offer higher performance search on larger-scale vector data at a lower cost than other options. But that one-size-fits-all approach of adding a “vector column” table isn’t optimized for managing embeddings, and as a result, treats them as second-class citizens. Some technologists have extended traditional relational databases to support embeddings. Embeddings capture profiles, products and search queries, and the searches will yield nearest-neighbor results, often aligning with consumer interests in an almost uncanny way. An app developer building a recommendation engine wants to be able to recommend new types of products that appeal to individual consumers. ![]() Recommendation systems - including user-generated content recommendation, personalized ecommerce search, video and image analysis, targeted advertising, antivirus cybersecurity, chatbots with improved language skills, drug discovery, protein search and banking anti-fraud detection - are among the first prominent use cases well managed by vector databases with speed and accuracy.Ĭonsider an ecommerce scenario where there are hundreds of millions of different products available. ![]() AI applications built on vector databases can analyze voluminous unstructured data for marketing, sales, research and security purposes. It’s increasingly common for a company’s comprehensive data strategy to include AI, but it’s vital to consider which business units and use cases will benefit most. Vector database strategy starts with use cases that make sense for your business These are powerful applications that can help a company meet its business objectives. What’s key in the market is that developers anywhere can now add a vector database, with its production-ready capabilities and lightning-fast search of unstructured data, to AI applications. The tool to store, index and search through these embeddings is a vector database - purpose-built to manage embeddings and their distinct structure. So, a well-trained neural network model will output embeddings that align with specific content and can be used to conduct a semantic similarity search. The resulting models turn each single piece of unstructured data into a list of floating point values - our search-enabling embedding. Sophisticated, widely used algorithms include STEGO for computer vision, CNN for image processing and Google’s BERT for natural language processing. To split important hairs a bit further, a model is the computational output of a machine learning (ML) algorithm (method or procedure) run on data. Quality data - and insightsĮmbeddings arise essentially as a computational byproduct of an AI model, or more specifically, a machine or deep learning model that’s trained on very large sets of quality input data. That means finding similar items based on nearest matches. These embeddings make split-second, scalable “similarity search” possible. They are numerical values - coordinates of sorts - representing unstructured data objects or features, like a component of a photograph, a portion of a person’s buying profile, select frames in a video, geospatial data or any item that doesn’t fit neatly into a relational database table. But a semantic search that understands the meaning and context of an image or other unstructured piece of data, as well as a search query, is virtually impossible with manual processes.Įnter embedding vectors, also called vector embeddings, feature vectors, or simply embeddings. Manual tagging lends itself to a traditional lexical search that matches words and strings exactly. Tags can be rife with not-so-obvious classifications and relationships. Terribly time-consuming, hit-or-miss ways of managing unstructured data often boil down to manually tagging the data (think labels and keywords on video platforms). Unstructured data - such as images, video, audio, and user behaviors - generally don’t fit the relational database model it can’t be easily sorted into row and column relationships. Vector databases offer a mind-numbing new level of capability to search unstructured data in particular, but can tackle semi-structured and even structured data as well. These mark a new category of database management and a paradigm shift for making use of the exponential volumes of unstructured data sitting untapped in object stores. But none of the data growth truly gets operationalized and democratized without the new kid on the block: vector databases. Well-designed AI-based applications sift through extremely large datasets extremely quickly to generate new insights and ultimately power new revenue streams, thus creating real value for businesses. ![]()
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