.png?width=22&height=21&name=Artificial%20Intelligence%20(AI).png){kind=small}
Generative AI is reshaping how businesses create, optimise, and scale digital solutions. This course introduces participants to foundational AI concepts, practical prompt engineering, and key tools such as ChatGPT and Copilot. They will also explore how generative AI operates across cloud platforms, with hands-on labs covering AWS, Google Cloud, and Microsoft Azure environments. Ethical considerations and best practices are woven throughout to support responsible innovation.
Learning Outcomes:
Understand the core principles of generative AI and its business applications
Compare and apply various generative AI models and prompt engineering techniques
Use tools such as ChatGPT and Copilot for content and code generation
Navigate multi-cloud environments for AI development and deployment
Evaluate data security and ethical risks in generative AI use
Key Topics:
Fundamentals of generative AI and foundation models
Prompt engineering and natural language processing with ChatGPT
Code generation using Copilot and AWS CodeWhisperer
Multi-cloud deployment with AWS, Azure, and Google Cloud
Data security and responsible AI practices
Hands-on labs including chatbot creation, code translation, and GenAI apps
DAY 1: Foundations of Generative AI and Application Scenarios
1. Introduction to Generative AI
- Differences between AI, Machine Learning, Deep Learning and GenAI
- Understand the basics of generative models and architecture components
- Explore business use cases and applied GenAI in:
- Healthcare
- Life Sciences
- Financial Services
- Manufacturing
- Retail
- Media Entertainment
- Architecture of GenAI application
2. What is Foundation Models?
- Overview on the various types of Foundation Models
- Language models and what ‘Large’ means
- Overview of Large Language Models (LLMs)
- Using Autocomplete as an example of using language models
- Example usage with LangChain
- Explore use cases for LLMs:
- Text generation
- Translation
- Question Answering
- Chatbots
- Sentiment analysis
3. GenAI Natural Language Processing - ChatGPT Prompt Engineering
- Learn how to effectively craft prompts for ChatGPT and other large language models (LLMs)
- Best practices for prompt design
4. GenAI Models
- Overview of Generative Models – GANs and VAEs
- Other Generative Models
- Comparisons of different models
5. GenAI Program Code Generation
- Using GenAI to translate code between languages
- Code optimization and refactoring
- Code Assistants: Amazon CodeWhisperer, Google DuetAI, Microsoft Copilot
DAY 2: Multi-Cloud Platforms for Generative AI
6. Multi-Cloud Platform Overview
- Dive into AWS, Google Cloud, and Azure platforms for GenAI development and tools
- Demonstration: Set up accounts, understand services, and explore GPU/TPU options
7. Protecting and Securing Your Data
- Overview of Data security when operating in clouds
- Monitoring and Operations
- Demonstration using AWS in protecting and securing your data
8. Governance, Ethics and the Future of AI
- Ethical concerns and considerations
- Responsible AI
- Where we are heading with Generative AI
9. Wrap-Up and Conclusion
HANDS-ON LABS
Lab 1: Building Chatbots with ChatGPT
- Use ChatGPT for basic interactions
- Experiment with different prompts to achieve desired responses
Lab 2: Explore GenAI with CoPilot
- Experiment with different prompts to achieve desired outcomes
- Designer, Vacation Planner, Music/Lyrics suggestions, Cooking Assistant fun exploration
Lab 3: Generating Code using CoPilot
- Use CoPilot for code generation
Lab 4: Using AWS Bedrock for Generative AI
- AWS Bedrock fundamentals
- Building Gen AI application
Lab 5: Code Generation and Website Builder
- AWS CodeWhisperer
- ChatGPT
- Google Gemini
.png?width=50&name=Artificial%20Intelligence%20(AI).png)
/AI%20(6)-1-1.png?width=360&name=AI%20(6)-1-1.png)
/AI%20(8)-1.png?width=360&name=AI%20(8)-1.png)
/AI%20(5)-1.png?width=360&name=AI%20(5)-1.png)
