This project aims to collect data from various internal systems, process the collected data, and store it in Amazon DynamoDB. The processed data is then used to fine-tune a Large Language Model (LLM) to improve its performance and adapt it to the specific domain and requirements of the organization.
The project consists of the following main components:
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Data Collection: Scripts and tools are developed to extract relevant data from internal systems, such as databases, log files, and APIs. The collected data is cleaned, transformed, and normalized to ensure consistency and compatibility.
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Data Processing: The collected data undergoes further processing to prepare it for storage and fine-tuning. This may include tasks such as data deduplication, anonymization, and feature extraction. The processed data is structured in a format suitable for ingestion by the LLM.
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Data Storage: The processed data is stored in Amazon DynamoDB, a highly scalable and flexible NoSQL database. DynamoDB provides fast and reliable access to the data, allowing efficient retrieval during the fine-tuning process.