Remove Data Discovery Remove Large Language Models Remove Metadata
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Unstructured data management and governance using AWS AI/ML and analytics services

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But most important of all, the assumed dormant value in the unstructured data is a question mark, which can only be answered after these sophisticated techniques have been applied. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly.

ML 167
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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. One of the challenges really is how these large language models are created.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. One of the challenges really is how these large language models are created.

article thumbnail

Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. One of the challenges really is how these large language models are created.

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IBM watsonx Platform: Compliance obligations to controls mapping

IBM Journey to AI blog

Large language models (LLMs) are becoming an integral part of a risk and compliance program, and they require little to no training. LRR and governance data is enhanced with the LLMs hosted in watsonx to apply the banks various process, risk and control taxonomies.

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Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context. This allows FMs to retain their inductive abilities while grounding their language understanding and generation in well-structured domain knowledge and logical reasoning.

Metadata 150