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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 developmentā€”from data discovery to model training. Database metadata can be expressed in various formats, including schema.org and DCAT.

Metadata 116
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IBM watsonx Platform: Compliance obligations to controls mapping

IBM Journey to AI blog

The enhanced metadata supports the matching categories to internal controls and other relevant policy and governance datasets. Integrated vectorized embedding capabilities streamline data preparation for various applications such as retrieval augmented generation (RAG) and other machine learning and generative AI use cases.

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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 ā€“ namely streamlining data discovery and access across data sources ā€“ and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 130
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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.

ETL 243
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AI thatā€™s ready for business starts with data thatā€™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.

Metadata 113
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Data architecture strategy for data quality

IBM Journey to AI blog

Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads. Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable.

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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.