Experimenting with Foundation Models

Overview

Explore practical, budget-friendly ways to experiment with Foundation Models, LLMs, and AI with expert guidance from AWS Hero Faye Ellis.

Syllabus

• Introduction to Foundation Models
Definition and significance of Foundation Models
Overview of popular Foundation Models (GPT, BERT, DALL-E, etc.)
Real-world applications and use cases
• Setting Up Your Experimentation Environment
Budget-friendly cloud solutions for AI experiments
Introduction to AWS tools for AI and machine learning
Guidelines for setting up a local ML environment
• Fundamentals of Large Language Models (LLMs)
Mechanics of LLMs and how they process data
Key features and capabilities of LLMs
Comparison of different LLM architectures
• Practical Techniques for Experimentation
Data preparation and processing for AI experiments
Fine-tuning pre-trained models: methods and best practices
Integrating LLMs into applications using APIs and SDKs
• Cost Optimization Strategies
Monitoring and managing cloud resources cost-effectively
Leveraging free tiers and credits on cloud platforms
Efficient use of computational resources
• Real-World Experimentation with Foundation Models
Designing experiments to test model capabilities
Evaluating performance and accuracy
Case studies of successful AI projects on a budget
• Troubleshooting and Debugging
Common issues in model experimentation and how to fix them
Techniques for interpreting model outputs and errors
• Ethics and Responsible AI Use
Understanding bias and fairness in AI models
Best practices for ensuring privacy and compliance
• Capstone Project
Designing and executing a comprehensive AI experiment
Presenting findings and proposing real-world applications
• Additional Resources and Further Learning
Recommended readings and online courses
Communities and forums for ongoing support and collaboration

Subjects

Computer Science