Marktechpost

MARCH 12, 2024

When building machine learning (ML) models using preexisting datasets, experts in the field must first familiarize themselves with the data, decipher its structure, and determine which subset to use as features. So much so that a basic barrier, the great range of data formats, is slowing advancement in ML.

Metadata 97

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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.

Metadata 130

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Marktechpost

JULY 19, 2023

In the actual world, machine learning (ML) systems can embed issues like societal prejudices and safety worries. Understanding patterns of model output for subgroups or slices of input data goes beyond examining aggregate metrics like accuracy or F1 score. Zeno works together with other systems and combines the methods of others.

Machine Learning 52

Machine Learning ML Python Metadata 52

Flipboard

OCTOBER 25, 2023

After decades of digitizing everything in your enterprise, you may have an enormous amount of data, but with dormant value. However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. These services write the output to a data lake.

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IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Platform 130

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ML @ CMU

MARCH 24, 2023

Behaviors are subgroups of data (typically defined by combinations of metadata) quantified by a specific metric. Succinctly, behavior-driven development requires sufficient data that is representative of expected behaviors and metadata for defining and quantifying the behaviors. Figure 5.

AI Developer 144

AI Developer AI Development Metadata AI 144

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Is more data always better? AR : Absolutely.

Large Language Models 52

Large Language Models Metadata AI AI 52