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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. On complementary side wrt to the software architect side; to enable faster deployment of LLMs researchers have proposed serverless inference systems.

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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. Triton has a very good track record and performance on multiple models deployed on a single endpoint,” he said.

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Watch Our Top Virtual Sessions from ODSC West 2023 Here

ODSC - Open Data Science

Gerard Kostin | Director of Data Science | DataGPT Delve into the capabilities of Large Language Models (LLMs) in data analytics, highlighting the inherent challenges when processing extensive datasets. This session gave attendees a hands-on experience to master the essential techniques.

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Large sequence models for software development activities

Google Research AI blog

Software engineering isn’t an isolated process, but a dialogue among human developers, code reviewers, bug reporters, software architects and tools, such as compilers, unit tests, linters and static analyzers. These innovations are already powering tools enjoyed by Google developers every day.

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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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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). Stanislav Yeshchenko is a Software Architect at Q4 Inc.

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Educating a New Generation of Workers

O'Reilly Media

Entirely new paradigms rise quickly: cloud computing, data engineering, machine learning engineering, mobile development, and large language models. To further complicate things, topics like cloud computing, software operations, and even AI don’t fit nicely within a university IT department.