Remove Automation Remove Data Integration Remove Metadata
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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

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Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. First, we explore the option of in-context learning, where the LLM generates the requested metadata without documentation.

Metadata 148
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Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

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In this post, we propose an end-to-end solution using Amazon Q Business to address similar enterprise data challenges, showcasing how it can streamline operations and enhance customer service across various industries. For the metadata file used in this example, we focus on boosting two key metadata attributes: _document_title and services.

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

IBM Journey to AI blog

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Metadata 188
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Applying generative AI to revolutionize telco network operations 

IBM Journey to AI blog

In addition to these capabilities, generative AI can revolutionize drive tests, optimize network resource allocation, automate fault detection, optimize truck rolls and enhance customer experience through personalized services. This aids in better data integration and utilization in the upper layers.

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Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines

AWS Machine Learning Blog

In the face of these challenges, MLOps offers an important path to shorten your time to production while increasing confidence in the quality of deployed workloads by automating governance processes. This post illustrates how to use common architecture principles to transition from a manual monitoring process to one that is automated.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

In addition, the Amazon Bedrock Knowledge Bases team worked closely with us to address several critical elements, including expanding embedding limits, managing the metadata limit (250 characters), testing different chunking methods, and syncing throughput to the knowledge base.

DevOps 83