Remove Document Remove Machine Learning Remove Metadata
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Deep automation in machine learning

O'Reilly on Data

In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. However, machine learning isn’t possible without data, and our tools for working with data aren’t adequate.

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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Amazon EMR provides a big data environment for data processing, interactive analysis, and machine learning using open source frameworks such as Apache Spark, Apache Hive, and Presto. Although LLMs can generate syntactically correct SQL queries, they still need the table metadata for writing accurate SQL query.

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Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

This is accomplished through tags, annotations, and metadata (TAM). So, there must be a strategy regarding who, what, when, where, why, and how is the organization’s content to be indexed, stored, accessed, delivered, used, and documented. Smart content includes labeled (tagged, annotated) metadata (TAM).

Strategy 267
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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. These partners are: Collibra – providing data governance and discovery (metadata, catalogs) across the entire data landscape.

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5 Benefits intelligent document processing brings to content management

CIO Business Intelligence

As explained in a previous post , with the advent of AI-based tools and intelligent document processing (IDP) systems, ECM tools can now go further by automating many processes that were once completely manual. That relieves users from having to fill out such fields themselves to classify documents, which they often don’t do well, if at all.

Insurance 116
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Manage access controls in generative AI-powered search applications using Amazon OpenSearch Service and Amazon Cognito

AWS Big Data

A common adoption pattern is to introduce document search tools to internal teams, especially advanced document searches based on semantic search. In a real-world scenario, organizations want to make sure their users access only documents they are entitled to access. The following diagram depicts the solution architecture.

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Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion

AWS Big Data

For agent-based solutions, see the agent-specific documentation for integration with OpenSearch Ingestion, such as Using an OpenSearch Ingestion pipeline with Fluent Bit. This includes adding common fields to associate metadata with the indexed documents, as well as parsing the log data to make data more searchable.

Metadata 118