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The term “foundation model” was coined by the Stanford Institute for Human-Centered ArtificialIntelligence in 2021. Fortunately, data stores serve as secure data repositories and enable foundation models to scale in both terms of their size and their training data.
This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of ArtificialIntelligence (AI) possible.
Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, dataclassification software, optical character recognition (OCR), document fingerprinting, and encryption.
Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, dataclassification, organization and tagging.
This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of ArtificialIntelligence (AI) possible.
So how does dataintelligence support governance? Examples of governance features that leverage dataintelligence include: A business glossary, with automated dataclassification, to align teams on key terms. Data lineage tracking and impact analysis reports to show transformation over time.
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