This repository contains all the materials, projects, and notes from the "Introducing Generative AI with AWS" course. The program provided a comprehensive journey from AI fundamentals to real-world implementation using AWS services.
Through hands-on projects and exercises, I gained practical experience in building, fine-tuning, and deploying generative AI models.
- Deep dive into AI evolution and the machine learning pipeline
- Hands-on experience with decision trees, neural networks, and generative vs. discriminative AI
- Understanding the relationship between AI, ML, and generative technologies
- Transformer-based architecture and LLM deep dive
- Advanced prompt engineering techniques
- Retrieval-Augmented Generation (RAG) implementation
- Fine-tuning pre-trained models
- Building LLMs with code and understanding encoder/decoder architecture
- Amazon SageMaker & Jupyter Notebook integration
- Hands-on project: PartyRock application
- AWS Educate Machine Learning Foundations exercises
- Model Cards for documentation and transparency
Studied AWS Responsible AI Framework covering six pillars:
- Fairness: Algorithmic bias detection & mitigation
- Explainability: Making AI decisions interpretable
- Privacy & Security: Secure data handling & AI deployment
- Robustness: Building reliable AI systems
- Governance: Oversight & accountability
- Transparency: Clear documentation of AI capabilities
- ✅ AWS Educate Machine Learning Foundations Badge
![AWS Badge]: (https://www.credly.com/badges/2ebea529-1336-44b5-a705-a79e02f0dfc2/public_url) - ✅ PartyRock App Project - Functional generative AI application
- ✅ Practical skills in prompt engineering, model fine-tuning, and ethical AI practices
- ✅ Certificate of Completion