About Sustainability, Generative AI, and Cloud Computing
Sustainability is a broad topic and has no one singular definition. Check out the UN Sustainable Development Goals as a starting point.
Generative AI refers to a type of artificial intelligence designed to generate new content, data, or outputs that are not explicitly programmed in advance. It involves models that can create new examples or samples within a given domain, such as images, text, music, or other types of data.
Cloud Computing is the practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than a local server or a personal computer. It allows you to focus on developing, rather than having to worry about providing all the hardware. One of the biggest cloud service providers out there is AWS.
- 8:00AM: Check in + Breakfast
- 8:30AM: Introduction
- 8:40AM: Icebreaker
- 9:10AM: Hacking commences
- 12:00PM: Lunch (provided)
- 5:00PM: Dinner (provided)
- 6:00PM: Hacking ends
- 6:10PM: Judging starts
- 7:45PM: Closing ceremony
- 8:00PM: End of Hackathon!
- UBC Card
- Adapters
- Laptop and charging cables
- A water bottle
- Reusable coffee mug, containers, and cutlery
Sauder Learning Labs: 6326 Agricultural Road, Vancouver, BC V6T 1Z2
It is behind the Sauder building and sandwiched between Triple O’s and the Leonard S. Klinck building. Look out for a sign that says David Lam Learning Centre!
- No plagiarism
- Code must be on GitHub and open sourced
- Any private datasets used must not contain personally identifiable information
- Project design and development must start at the hackathon’s beginning, but preprocessed and structured data is allowed
- Total 5 minutes (3 min presentation, 2 min Q&A)
- We recommend talking about your motivation for choosing this project, and its potential impact.
- REQUIRED: To judge the technical details of your solution, you must include an architecture diagram (try out draw.io, or any other tool).
- REQUIRED: You must explain why your solution addresses an issue regarding sustainability.
- DEADLINE: There is a hard deadline to submit the link to your public GitHub repository in your Discord team channel by 6:00PM. Late submissions will lead to disqualification.
- Creativity and Originality: The innovativeness and uniqueness of the generated solution.
- Technical Implementation: The complexity and effectiveness of the AI model and its integration with the user interface.
- User Interaction: The intuitiveness and effectiveness of the user interface in influencing the generated solution.
- Cloud deployment: The choices and efficient deployment of cloud services for their solution.
- Sustainability: The extent to which the solution addresses an issue regarding sustainability.
- Presentation: The clarity, coherence, and persuasiveness of the final presentation.
For frequently asked questions and tips, please visit FAQs
In the week leading up to the event, a workshop on Amazon Bedrock will be posted.
Getting Started With AWS Workshop Studio
- Introduction to Generative AI - Art of the Possible
- Planning a Generative AI Project
- Foundations of Prompt Engineering
- Generative AI with Large Language Models
- Introduction to LangChain - LangChain is a framework for developing applications powered by language models
- Serverless LLM apps with Amazon Bedrock - (Course) Enroll for free. Learn how to deploy a large language model-based application into production using serverless technology.
- Generative AI with Large Language Models - (Course) Enroll for free. Excellent intro course. Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works.
- Prompt Engineering Best Practices - Prompt engineering best practices for LLMs on Amazon Bedrock.
- General Prompt Engineering using Party Rock - Learn General Prompt Engineering using Party Rock (free to use).
- Anthropic Claude on Party Rock - Learn Anthropic Claude Interactive Prompt Engineering tutorial on Party Rock (free to use).
- Anthropic’s Official Documentation - Anthropic’s Official Prompting Documentation
- AWS Bedrock Samples Repository - AWS's Official GitHub Samples Repository
- AWS GenAI Quick Starts - AWS's Quick Starts for GenAI Repository
- AWS Bedrock PDF Chat - Example of PDF Chat using Amazon Bedrock
Retrieval-augmented generation (RAG) for large language models (LLMs) aims to improve prediction quality by using an external datastore at inference time to build a richer prompt that includes some combination of context, history, and recent/relevant knowledge
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More in-depth intro Retrieval Augmented Generation (RAG) for LLMs
- Building AI-powered search in PostgreSQL using Amazon SageMaker and pgvector (Blog post)
- AWS Samples (GitHub) - RAG with Amazon Bedrock and PGVector on Amazon RDS
- Knowledge Bases now delivers fully managed RAG experience in Amazon Bedrock
- Knowledge Base for Amazon Bedrock - Documentation
- Amazon OpenSearch Service’s vector database capabilities explained
- Build scalable and serverless RAG workflows with a vector engine for Amazon OpenSearch Serverless and Amazon Bedrock Claude models (Blog post)
Enable generative AI applications to execute multistep tasks across company systems and data sources
- User Guide
- Demo Video - Agents for Amazon Bedrock
- Amazon Bedrock Agents Quickstart - Functional code example
- Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock
- AWS Cloud Essentials
- AWS Security Essentials
- AWS Networking Essentials
- AWS Technical Essentials
- Architecting on AWS - Online Course Supplement
- AWS Serverless Land - AWS Serverless examples, patterns, documentation and guidance.
Where do current tools fall short? How can you innovate using generative AI to fill those gaps? Here are some examples to get you started.
A web-based tool developed by Google to help users practice answering common interview questions in a variety of fields, including tech, data, and UX. It uses speech recognition and AI feedback to analyze users' responses, helping them identify strengths and areas for improvement.
This mobile app enhances online shopping by analyzing the materials and production methods of clothing items. It then suggests eco-friendly alternatives that match the user's style, offering similar articles of clothing made from sustainable materials and produced through ethical practices