Primary care and pediatric providers can spend significant time manually cross-referencing multiple sources to construct accelerated catch-up protocols for unvaccinated or under-vaccinated patients. The Pediatric Vaccine Assistant streamlines this workflow, reducing administrative burden and supporting evidence-based care.

The recruitment of patients for clinical trials presents significant challenges for pharmaceutical companies, often delaying drug development, increasing cost, and leaded to study failure. This lab explores how AI applied to anonymized health records enables efficient trial matching—at single, bulk, and multi-trial levels—reducing time, improving accuracy, and enhancing access for researchers, providers, and patients.

The WWT AI-Powered Predictive Maintenance Assistant uses machine learning and a large language model to predict equipment failures, schedule maintenance, generate instructions, and answer related questions. Through an interactive UI, users explore proactive, data-driven strategies to reduce downtime, extend machine life, and optimize resources, demonstrating AI's transformative potential in manufacturing environments.

This lab will help users strengthen their prompt engineering skills using the prompt blueprint framework, which is a structured approach for writing effective prompts. Participants will refine prompts step by step and experiment with blueprint elements to achieve better model outputs.

This lab deploys a real-time speech-to-speech support agent for fictional company AnyTelco, where a customer calls in for help. It uses Amazon Nova Sonic in Amazon Bedrock for natural conversation, with S3/CloudFront and Cognito for frontend/auth. A backend on ECS/NLB streams audio and invokes MCP tools (e.g., DynamoDB customer profiles, Bedrock knowledge bases). All infrastructure is provisioned via AWS CDK.