The vast majority of the world’s data remains untapped, and enterprises are looking to generate value from this data by creating the next wave of generative AI applications that will make a transformative business impact. Retrieval-augmented generation (RAG) pipelines are a key part of this, enabling users to have conversations with large corpuses of data and turning manuals, policy documents…
]]>In the era of generative AI, vector databases have become indispensable for storing and querying high-dimensional data efficiently. However, like all databases, vector databases are vulnerable to a range of attacks, including cyber threats, phishing attempts, and unauthorized access. This vulnerability is particularly concerning considering that these databases often contain sensitive and…
]]>Recommendation models have progressed rapidly in recent years due to advances in deep learning and the use of vector embeddings. The growing complexity of these models demands robust systems to support them, which can be challenging to deploy and maintain in production. In the paper Monolith: Real Time Recommendation System With Collisionless Embedding Table, ByteDance details how they built…
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