AWS Web3 Blog
Author: Shankar Subramaniam
Shankar Subramaniam is a Senior Partner Solutions Architect in the AWS Partner Organization aligned with Strategic Partnership Collaboration and Governance (SPCG) engagements. Other contribution areas include Machine Learning and AWS Managed Service Provider (MSP) partners in EMEA.
Use a DAO to govern LLM training data, Part 4: MetaMask authentication
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In Part 3, we set up Amazon API Gateway and deployed AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job, creating a seamless data flow from IPFS to the knowledge base. In this post, we demonstrate how to configure MetaMask authentication, create a frontend interface, and test the solution.
Use a DAO to govern LLM training data, Part 3: From IPFS to the knowledge base
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia testnet using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In this post, we set up Amazon API Gateway and deploy AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job.
Use a DAO to govern LLM training data, Part 2: The smart contract
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, specifically focusing on the ingestion of training data. In this post, we focus on the writing and deployment of the Ethereum smart contract that contains the outcome of the DAO decisions.
Use a DAO to govern LLM training data, Part 1: Retrieval Augmented Generation
Blockchain and generative AI are two technical fields that have received a lot of attention in the recent years. There is an emerging set of use cases that can benefit from these two technologies. In this four-part series, we build a solution that governs the training data ingestion process of an AI model, using a smart contract and serverless components. We guide you through the different steps to build the solution. In this post, we review the overall architecture of the solution, and set up a large language model (LLM) knowledge base.