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Google AI Introduces Croissant: A Metadata Format for Machine Learning-Ready Datasets

Marktechpost

Database metadata can be expressed in various formats, including schema.org and DCAT. Unfortunately, these formats weren’t made with machine learning data in mind. Google has recently introduced Croissant, a new format for metadata in ML-ready datasets. Users can then publish their datasets.

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How the right data and AI foundation can empower a successful ESG strategy

IBM Journey to AI blog

That is, it should support both sound data governance —such as allowing access only by authorized processes and stakeholders—and provide oversight into the use and trustworthiness of AI through transparency and explainability.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

Data platform architecture has an interesting history. A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution.

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Data Fabric & Data Mesh: Two Approaches, One Data-Driven Destiny

Heartbeat

This data source may be related to the sales sector, the manufacturing industry, finance, health, and R&D… Briefly, I am talking about a field-specific data source. The domain of the data. Regardless, the data fabric must be consistent for all its components. Data fabric needs metadata management maturity.

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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning Blog

You will see an Amazon Simple Storage Service (Amazon S3) link to a metadata file. To discover the schema to be used while invoking the API from Einstein Studio, choose Information in the navigation pane of the Model Registry. Copy and paste the link into a new browser tab URL. Let’s look at the file without downloading it.

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US Open heralds new era of fan engagement with watsonx and generative AI

IBM Journey to AI blog

Year after year, IBM Consulting works with the United States Tennis Association (USTA) to transform massive amounts of data into meaningful insight for tennis fans. This year, the USTA is using watsonx , IBM’s new AI and data platform for business.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

Data lake foundations This module helps data lake admins set up a data lake to ingest data, curate datasets, and use the AWS Lake Formation governance model for managing fine-grained data access across accounts and users using a centralized data catalog, data access policies, and tag-based access controls.

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