Remove Blog Remove Data Classification Remove ETL
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Transforming network operations with AI: How Swisscom built a network assistant using Amazon Bedrock

AWS Machine Learning Blog

This following diagram illustrates the enhanced data extract, transform, and load (ETL) pipeline interaction with Amazon Bedrock. To achieve the desired accuracy in KPI calculations, the data pipeline was refined to achieve consistent and precise performance, which leads to meaningful insights.

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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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Transform raw insurance data into CSV format acceptable to Neptune Bulk Loader , using an AWS Glue extract, transform, and load (ETL) job. When the data is in CSV format, use an Amazon SageMaker Jupyter notebook to run a PySpark script to load the raw data into Neptune and visualize it in a Jupyter notebook.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning Blog

model_id = "anthropic.claude-3-5-sonnet-20240620-v1:0" # Load the prompt from a file (showed and explained later in the blog) with open('prompt.txt', 'r') as file: data = file.read() def callBedrock(body): # Format the request payload using the model's native structure. The same ETL workflows were running fine before the upgrade.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

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Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

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