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Success of AI in academic libraries depends on underlying data

CILIP

Success of AI in academic libraries depends on good underlying data. nder, scientific information specialist: Success of AI in academic libraries depends on good underlying data. Why do we hear so little in this respect from libraries on this side of the Atlantic? Q&A with Stephan Holl?nder,

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

However, data scientists should monitor results gathered through unsupervised learning. Because these techniques are making assumptions about the data being input, it is possible for them to incorrectly label anomalies. Isolation forest models can be found on the free machine learning library for Python, scikit-learn.

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Information Governance Innovations in 2019

Everteam

The intersection of Structured and Unstructured Data. Today there is a clear separation on how you manage structured data (database, transactional data) and unstructured data (documents, text, videos, images, email, social media, etc.). Data on legal hold = <1%. Record-worthy data = <2%.

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The Information Management Umbrella

Brandeis Records Manager

Your industry may dictate your relationship with your library people, if you even have a relationship with them. In academia, records management tends (not exclusively) to be grouped organizationally with library and archival units. In one sense, we are the Charlie Brown of an academic library department.

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Five benefits of a data catalog

IBM Big Data Hub

Imagine walking into the largest library you’ve ever seen. Fortunately, the library has a computer at the front desk you can use to search its entire inventory by title, author, genre, and more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Do I Need a Data Catalog?

erwin

A data catalog uses metadata, data that describes or summarizes data, to create an informative and searchable inventory of all data assets in an organization. Another classic example is the online or card catalog at a library. Sales are measured down to a zip code territory level across product categories.

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How generative AI delivers value to insurance companies and their customers

IBM Big Data Hub

IBM’s watsonx.ai™ foundation model library contains both IBM-built foundation models, as well as several open-source large language models (LLMs) from Hugging Face. Foundation models are becoming an essential ingredient of new AI-based workflows, and IBM Watson® products have been using foundation models since 2020.

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