Training on Generative AI Fundamentals and Applications

Module 1: Understanding Generative AI Concepts and Models

• Defining generative AI and its core concepts
• Understanding how generative models learn and create
• Exploring different types of generative models: GANs, VAEs, diffusion
• Differentiating between generative and discriminative AI
• Assessing the capabilities and limitations of generative AI
• Case Study: Exploring a generative AI application

Module 2: Foundation Models: GPT, DALL-E, and IBM Granite

• Understanding foundation models and their significance
• Exploring GPT architecture and its applications
• Understanding DALL-E for image generation
• Introducing IBM Granite and its capabilities
• Comparing foundation models and their use cases
• Case Study: Comparing the outputs of different foundation models

Module 3: Powerful Prompt Engineering Techniques

• Understanding the principles of effective prompt engineering
• Learning zero-shot, few-shot, and chain-of-thought prompting
• Designing prompts for specific generative AI tasks
• Iterating and refining prompts for better results
• Building prompt libraries for repeatable tasks
• Case Study: Applying prompt engineering to a generative AI task

Module 4: Writing Effective Prompts to Produce Desired Outcomes

• Understanding the elements of an effective prompt
• Crafting clear, specific, and actionable prompts
• Using constraints and parameters to guide outputs
• Avoiding common prompt pitfalls and errors
• Evaluating and improving prompt effectiveness
• Case Study: Writing and refining prompts for a business application

Module 5: Building Blocks of Generative AI

• Understanding the data requirements for generative AI
• Exploring model architectures and training processes
• Understanding the role of computation and infrastructure
• Assessing the cost and resource implications of generative AI
• Building a foundation for advanced generative AI work
• Case Study: Understanding the building blocks of a generative AI system

Module 6: Ethical Implications and Considerations of Generative AI

• Identifying ethical challenges in generative AI
• Understanding bias, fairness, and accountability in generative AI
• Addressing privacy and security concerns in generative AI
• Developing responsible generative AI practices
• Building ethical generative AI systems
• Case Study: Analyzing ethical considerations in a generative AI application

Module 7: Hands-On Labs with IBM watsonx.ai

• Getting started with IBM watsonx.ai platform
• Exploring generative AI capabilities on watsonx.ai
• Building and deploying generative AI models on watsonx.ai
• Integrating watsonx.ai with business applications
• Best practices for watsonx.ai usage
• Case Study: Building a generative AI application on watsonx.ai

Module 8: Hands-On Labs with OpenAI ChatGPT

• Getting started with OpenAI ChatGPT
• Exploring ChatGPT capabilities and features
• Building applications with ChatGPT APIs
• Customizing ChatGPT for specific use cases
• Best practices for ChatGPT usage
• Case Study: Building a ChatGPT application for a business need

Module 9: Hands-On Labs with Stable Diffusion and Hugging Face

• Getting started with Stable Diffusion for image generation
• Exploring Hugging Face for generative AI models
• Building generative AI applications with Hugging Face
• Customizing and fine-tuning models on Hugging Face
• Integrating Hugging Face with other AI tools
• Case Study: Building a generative AI application with Hugging Face

Module 10: Real-World Applications and Case Studies

• Analyzing successful generative AI implementations
• Identifying lessons learned and best practices
• Exploring emerging applications and trends in generative AI
• Developing a generative AI implementation plan
• Building a business case for generative AI adoption
• Case Study: Developing a generative AI implementation strategy