Abstract: Vector databases typically manage large collections of embedding vectors. As AI applications are growing rapidly, the number of embeddings that need to be stored and indexed is increasing.

This isn't a tutorial or a side project. This is a system I designed and deployed in a real-world production environment to solve a genuine business problem: enabling intelligent, context-aware search ...

Git isn't hard to learn, and when you combine Git and GitHub, you've just made the learning process significantly easier. This two-hour Git and GitHub video tutorial shows you how to get started with ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

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NVIDIA's cuVS integration with Faiss enhances GPU-accelerated vector search, offering faster index builds and lower search latency, crucial for managing large datasets. As the demand for processing ...

ABSTRACT: As the integration of Large Language Models (LLMs) into scientific R&D accelerates, the associated privacy risks become increasingly critical. Scientific NoSQL repositories, which often ...

Accomplished technology leader with 20+ years of experience in AI, Distributed systems and cloud technologies. computing, and AI/ML infrastructure. Accomplished technology leader with 20+ years of ...