Categorization ETL Prompt Engineering 
AWS Machine Learning Blog
MARCH 27, 2025
In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. For a multiclass classification problem such as support case root cause categorization, this challenge compounds many fold.
AWS Machine Learning Blog
JUNE 25, 2024
Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.
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AWS Machine Learning Blog
SEPTEMBER 1, 2023
These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.
AWS Machine Learning Blog
JANUARY 7, 2025
Machine learning (ML) classification models offer improved categorization, but introduce complexity by requiring separate, specialized models for classification, entity extraction, and response generation, each with its own training data and contextual limitations. Jumana Nagaria is a Prototyping Architect at AWS, based in London.
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