Data Cleaning & Preparation for LLM Training on AWS

As described on AWS Marketplace.

The Data Cleaning & Preparation for LLM Training Assessment helps organizations prepare high-quality, trusted datasets for training, fine-tuning, and retrieval-augmented generation (RAG) workflows. Delivered by senior AWS and AI specialists, this engagement evaluates data quality, structure, governance, and security to ensure your data is ready for use with large language models on AWS.

During the assessment, Cloud Catalysts reviews structured and unstructured data sources, including documents, transcripts, logs, and knowledge bases. We evaluate data completeness, accuracy, duplication, labeling, and relevance, while identifying issues that commonly degrade LLM performance such as noise, inconsistencies, and sensitive data exposure. The assessment also reviews data ingestion pipelines, preprocessing steps, and storage patterns to ensure scalability and cost efficiency.

Highlighted by the publisher on AWS Marketplace.

LLM-Ready Data Quality: Identify and remediate data quality issues that impact model accuracy, relevance, and reliability.

Secure & Governed Data Pipelines: Ensure sensitive data is protected through proper classification, access controls, and governance aligned with AWS best practices.

Actionable Preparation Roadmap: Receive clear recommendations for data cleaning, enrichment, labeling, and preprocessing to accelerate LLM training and deployment.