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Google AI Introduces Croissant: A Metadata Format for Machine Learning-Ready Datasets

Marktechpost

Even among datasets that include the same subject matter, there is no standard layout of files or data formats. This obstacle lowers productivity through machine learning development—from data discovery to model training. Database metadata can be expressed in various formats, including schema.org and DCAT.

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Five benefits of a data catalog

IBM Journey to AI blog

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Unstructured data management and governance using AWS AI/ML and analytics services

Flipboard

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.

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Towards Behavior-Driven AI Development

ML @ CMU

Behaviors are subgroups of data (typically defined by combinations of metadata) quantified by a specific metric. Succinctly, behavior-driven development requires sufficient data that is representative of expected behaviors and metadata for defining and quantifying the behaviors. Figure 5.

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Datasets at your fingertips in Google Search

Google Research AI blog

Dataset Search shows users essential metadata about datasets and previews of the data where available. Users can then follow the links to the data repositories that host the datasets. Dataset Search primarily indexes dataset pages on the Web that contain schema.org structured data.

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Build trust in banking with data lineage

IBM Journey to AI blog

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

Data fabric promotes data discoverability. Here, data assets can be published into categories, creating an enterprise-wide data marketplace. This marketplace provides a search mechanism, utilizing metadata and a knowledge graph to enable asset discovery. data virtualization) play a key role.