Data Discovery Metadata ML 
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.
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.
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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.
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. ”.
IBM Journey to AI blog
JULY 3, 2024
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.
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.
AWS Machine Learning Blog
MAY 31, 2024
Also consider storing the metadata of the files being loaded in your knowledge bases for effective tracking. You can also use custom data identifiers to create data identifiers tailored to your specific use case. Data discovery and findability Findability is an important step of the process.
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