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Articles by Mickael Ide
Accelerating Vector Search: Fine-Tuning GPU Index Algorithms
The first post in this series introduced vector search indexes, explained the role they play in enabling a widespread range of important applications, and provided a brief overview of vector search on the GPU with the RAFT library. 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.
Accelerating Vector Search: Fine-Tuning GPU Index Algorithms
The first post in this series introduced vector search indexes, explained the role they play in enabling a widespread range of important applications, and provided a brief overview of vector search on the GPU with the RAFT library. 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.
Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT
In the AI landscape of 2023, 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.
Text Classification with Naive Bayes Algorithms
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, and NB classifiers make no exception.
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