Remove Data Discovery Remove Information Remove Metadata
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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.

Metadata 118
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Unstructured data management and governance using AWS AI/ML and analytics services

Flipboard

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Unstructured information may have a little or a lot of structure but in ways that are unexpected or inconsistent. Text, images, audio, and videos are common examples of unstructured data.

ML 166
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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? Banks and their employees place trust in their risk models to help ensure the bank maintains liquidity even in the worst of times.

ETL 217
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Five benefits of a data catalog

IBM Journey to AI blog

So, instead of wandering the aisles in hopes you’ll stumble across the book, you can walk straight to it and get the information you want much faster. 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.

Metadata 130
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Datasets at your fingertips in Google Search

Google Research AI blog

For one example, in the United States a recent new policy requires free and equitable access to outcomes of all federally funded research, including data and statistical information along with publications. Dataset Search shows users essential metadata about datasets and previews of the data where available.

Metadata 116
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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

It can include technologies that range from Oracle, Teradata and Apache Hadoop to Snowflake on Azure, RedShift on AWS or MS SQL in the on-premises data center, to name just a few. All phases of the data-information lifecycle. The data fabric embraces all phases of the data-information-insight lifecycle.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata.