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Unbundling the Graph in GraphRAG

O'Reilly on Data

decomposes a complex task into a graph of subtasks, then uses LLMs to answer the subtasks while optimizing for costs across the graph. Then connect the graph nodes and relations extracted from unstructured data sources, reusing the results of entity resolution to disambiguate terms within the domain context.

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TensorFlow Object Detection — 1.0 & 2.0: Train, Export, Optimize (TensorRT), Infer (Jetson Nano)

Analytics Vidhya

Train, Export, Optimize (TensorRT), Infer (Jetson Nano) appeared first on Analytics Vidhya. Part 1 — Detailed steps from training a detector on a custom dataset to inferencing on jetson nano board or cloud using TensorFlow 1.15. The post TensorFlow Object Detection — 1.0 & 2.0:

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. Ive seen this firsthand.

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Generative AI is pushing unstructured data to center stage

CIO Business Intelligence

When I think about unstructured data, I see my colleague Rob Gerbrandt (an information governance genius) walking into a customer’s conference room where tubes of core samples line three walls. While most of us would see dirt and rock, Rob sees unstructured data. have encouraged the creation of unstructured data.

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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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How AI orchestration has become more important than the models themselves

CIO Business Intelligence

Choreographing data, AI, and enterprise workflows While vertical AI solves for the accuracy, speed, and cost-related challenges associated with large-scale GenAI implementation, it still does not solve for building an end-to-end workflow on its own. to autonomously address lost card calls.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO Business Intelligence

Some challenges include data infrastructure that allows scaling and optimizing for AI; data management to inform AI workflows where data lives and how it can be used; and associated data services that help data scientists protect AI workflows and keep their models clean. Through relentless innovation.