In this post, we dive deeper into each of the GPU-accelerated indexes mentioned in part 1 and give a brief explanation of how the algorithms work, along with a summary of important parameters to fine-tune their behavior. We then go through a simple end-to-end example to demonstrate cuVS’ Python APIs on a question-and-answer problem with a pretrained large language model and provide a…
]]>In the current AI landscape, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI. Semantic vector search enables a broad range of important tasks like detecting fraudulent transactions, recommending products to users, using contextual information to augment full-text searches, and finding actors that pose potential security risks.
]]>Naive Bayes (NB) is a simple but powerful probabilistic classification technique that parallelizes well and can scale to datasets of massive size. If you have been working with text processing tasks in data science, you know that machine learning models can take a long time to train. Using GPU-accelerated computing on those models has often resulted in significant gains in time performance…
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