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Create ultra-low latency feature store with ElastiCache for Redis
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Red Hat OpenShift AI: Customer success stories
In a digital world, your data can be a critical competitive advantage, but collecting data is only the starting point—how you use your data is the real differentiator.
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) can help you harness your data to deliver business insights, automate tasks, and advance system capabilities… but only if your environment provides the necessary support.
Download this e-book to learn the 6 essential steps to building a production-ready AI/ML environment.
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Why databases are at the heart of GenAI apps
Generative AI is revolutionizing innovation, and yet implementing GenAI isn’t as easy as flicking a switch. According to Google’s Data & AI Trends report, 86% of organizations recognize that delivering contextual and relevant user experiences through GenAI-integrated databases has a substantial positive impact. However, only 14% of those folks are satisfied with legacy database performance.
This white paper examines how operational databases are central to creating effective enterprise GenAI applications.
Discover how to get started by reading on, as well as how Google Cloud's database and AI services can boost your GenAI projects’ effectiveness.
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Infinidat Cures RAG Inferencing Response Time Troubles
Generative AI (GenAI) and Large Language Models (LLMs) offer equal opportunities as they do challenges. These AI systems can perform various language tasks but may "hallucinate," providing inaccurate information. Mitigating this is crucial.
This paper explores the boons of Retrieval-Augmented Generation (RAG) inferencing and how to mitigate all-too-common RAG storage latency bottlenecks.
Download your copy to address these RAG inferencing issues and see how Infinidat’s Neural Cache technology address RAG latency and enhances LLM performance.
