Data Integration Data Quality Explainability Metadata 
IBM Journey to AI blog
DECEMBER 16, 2022
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.
IBM Journey to AI blog
OCTOBER 12, 2023
Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.) Learn more about IBM watsonx 1.
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The MLOps Blog
JUNE 27, 2023
This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Integration with ML tools and libraries: Provide you with flexibility and extensibility. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you render audio/video?
Pickl AI
JULY 23, 2023
It requires sophisticated tools and algorithms to derive meaningful patterns and trends from the sheer magnitude of data. Meta Data Metadata, often dubbed “data about data,” provides essential context and descriptions for other datasets. It helps in gaining insights into past performance.
IBM Journey to AI blog
AUGUST 4, 2023
By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.
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