Q: What are some common use cases for Generative AI?
A: Intelligent search, automated customer-support chatbots, dialog summarization, not-safe-for-work (NSFW) content moderation, personalized product videos, source code generation, and others.
Q: How do project life cycle phases impact Generative AI development?
A: The project life cycle includes stages like defining a use case, prompt engineering, selecting a foundation model, fine-tuning, aligning with human values, deploying the model, and integrating with external data sources, which impact generative AI development.
Q: How are foundation models and model hubs important in Generative AI?
A: Foundation models are large and complex neural network models with billions of parameters, trained on massive data. Model hubs, like Hugging Face Model Hub, PyTorch Hub, or Amazon SageMaker JumpStart, offer a collection of models with detailed descriptions and use cases, providing a starting point for generative AI projects.
Q: Describe the Generative AI project life cycle?
A: The generative AI project life cycle, though not definitive, guides through important parts of the application journey. It helps in gaining intuition, avoiding potential difficulties, and improving decision-making at each step.
Q: What makes AWS a suitable platform for building Generative AI foundation models?
A: AWS offers a range of frameworks and infrastructure, including optimized compute instances for building foundation models, making it suitable for using generative AI with complex entities like human language, images, videos, and audio clips.
Q: How does Generative AI on AWS differ from other platforms?
A: AWS offers increased flexibility, choice, enterprise-grade security, state-of-the-art generative AI capabilities, low operational overhead through fully managed services, and quick access to ready-to-use solutions. AWS allows developers and scientists to build scalable and secure generative AI applications quickly and safely.
- Chapter 1 - Generative AI Use Cases, Fundamentals, Project Lifecycle
- Chapter 2 - Prompt Engineering and In-Context Learning
- Chapter 3 - Large-Language Foundation Models
- Chapter 4 - Quantization and Distributed Computing
- Chapter 5 - Fine-Tuning and Evaluation
- Chapter 6 - Parameter-efficient Fine Tuning (PEFT)
- Chapter 7 - Fine-tuning using Reinforcement Learning with RLHF
- Chapter 8 - Optimize and Deploy Generative AI Applications
- Chapter 9 - Retrieval Augmented Generation (RAG) and Agents
- Chapter 10 - Multimodal Foundation Models
- Chapter 11 - Controlled Generation and Fine-Tuning with Stable Diffusion
- Chapter 12 - Amazon Bedrock Managed Service for Generative AI
- YouTube Channel: https://youtube.generativeaionaws.com
- Generative AI on AWS Meetup (Global, Virtual): https://meetup.generativeaionaws.com
- Generative AI on AWS O'Reilly Book: https://www.amazon.com/Generative-AI-AWS-Multimodal-Applications/dp/1098159225/
- Data Science on AWS O'Reilly Book: https://www.amazon.com/Data-Science-AWS-End-End/dp/1492079391/