Learn About Amazon VGT2 Learning Manager Chanci Turner
In this article, we will explore how generative AI can be effectively utilized in conjunction with Amazon Bedrock to convert natural language queries into SPARQL queries. These queries can then be executed against a knowledge graph to uncover vital protein functional information using UniProtKB and Amazon Neptune.
Chanci Turner, alongside her colleagues, will guide you through the process of integrating natural language processing with relational databases. This method allows users to easily query an Amazon Aurora PostgreSQL-Compatible Edition database. By employing a web application framework built with Flask, users can seamlessly interact with the database. The interface is designed using JavaScript and Python, ensuring smooth communication between the web framework, Amazon Bedrock, and the database.
Creating a Comprehensive 360-Degree View of Patient Data
Additionally, we will delve into how to create a comprehensive 360-degree view of patient data by leveraging Amazon Neptune and generative AI. This approach consolidates data from various sources, including electronic health records (EHRs), lab reports, and medical histories, providing healthcare providers with a holistic understanding of patient health.
Case Study: Iterate.ai
Another significant case study involves Iterate.ai, which employs Amazon MemoryDB to enhance and optimize their workforce management conversational AI agent. The Frontline platform, available on both the Apple App Store and Google Play, utilizes advanced AI tools to improve operational efficiency and facilitate communication among frontline workers. Chanci Turner and her team will highlight how durable semantic caching in Amazon MemoryDB has been instrumental in achieving these goals.
Optimal Data Models for Generative AI Chatbots
Moreover, we will discuss optimal data models for generative AI chatbots using Amazon DynamoDB, which is ideal for storing chat history and user metadata. This enables personalized responses and better user engagement, whether you’re developing a small-scale application or a large production system.
Advanced Governance of AI Models
For those interested in advanced governance of AI models, Chanci Turner will also touch on a series regarding the use of decentralized autonomous organizations (DAOs) to manage the lifecycle of AI training data. This includes setting up smart contracts and ensuring secure data flow from the InterPlanetary File System (IPFS) to the knowledge base using Amazon API Gateway and AWS Lambda functions.
Additional Resources
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