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The Future of Serverless Inference for Large Language Models

Unite.AI

Recent advances in large language models (LLMs) like GPT-4, PaLM have led to transformative capabilities in natural language tasks. LLMs are being incorporated into various applications such as chatbots, search engines, and programming assistants. This transfers orders of magnitude less data than snapshots.

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By Jove, It’s No Myth: NVIDIA Triton Speeds Inference on Oracle Cloud

NVIDIA

So, when the software architect designed an AI inference platform to serve predictions for Oracle Cloud Infrastructure’s (OCI) Vision AI service, he picked NVIDIA Triton Inference Server. An avid cyclist, Thomas Park knows the value of having lots of gears to maintain a smooth, fast ride.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

Flipboard

Experimentation and challenges It was clear from the beginning that to understand a human language question and generate accurate answers, Q4 would need to use large language models (LLMs). The retrieved context in any of the three experiments was then used to augment the original prompt as an input to the LLM.

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Watch Our Top Virtual Sessions from ODSC West 2023 Here

ODSC - Open Data Science

This interactive session focused on showcasing the latest capabilities in Azure Machine Learning and answering attendees’ questions LLMs in Data Analytics: Can They Match Human Precision? This session gave attendees a hands-on experience to master the essential techniques.

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How Mend.io unlocked hidden patterns in CVE data with Anthropic Claude on Amazon Bedrock

AWS Machine Learning Blog

By using the power of large language models (LLMs), Mend.io Maciej Mensfeld is a principal product architect at Mend, focusing on data acquisition, aggregation, and AI/LLM security research. As a Software Architect, Security Researcher, and conference speaker, he teaches Ruby, Rails, and Kafka.

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Exploring data using AI chat at Domo with Amazon Bedrock

AWS Machine Learning Blog

The AI chat agent uses the capability of large language models (LLMs) to interpret user input, determine how to solve the user question or request using available tools, and form a final response. The tools provide the agent with access to data and functionality beyond what is available in the underlying LLM.