Remove Business Intelligence Remove Metadata Remove ML
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). Capture and document model metadata for report generation. Increase trust in AI outcomes.

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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality. Integrate governance with business strategy Data governance should be tightly aligned with broader IT policies and business strategies.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging. .

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A Beginner’s Guide to Data Warehousing

Unite.AI

Data warehousing is a data management system to support Business Intelligence (BI) operations. Moreover, modern data warehousing pipelines are suitable for growth forecasting and predictive analysis using artificial intelligence (AI) and machine learning (ML) techniques. Metadata: Metadata is data about the data.

Metadata 162
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Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning Blog

Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the prediction of future events. You can add metadata to the policy by attaching tags as key-value pairs, then choose Next: Review. Choose Next: Tags.

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Five benefits of a data catalog

IBM Journey to AI blog

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning Blog

Across 180 countries, millions of developers and hundreds of thousands of businesses use Twilio to create personalized experiences for their customers. As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads.

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