Generative AI for Technical Managers (GAITM)

Generative AI Fundamentals

• What is Generative AI
• Key Concepts in Generative AI
• Generative Models and Discriminative Models
• Types of Generative Models (e.g., Variational Autoencoders, Generative Adversarial Networks)
• Training and Inference in Generative Models
• Discuss few Industry use cases of Generative AI Applications
  • Applications of Generative AI and Deep Learning
    • Image and Video Generation
    • Music and Audio Generation
• Text Generation
• Lab: Hands on lab on Text Generation using Large Language models

Introduction to Large Language Models (LLMs)

• What are Large Language Models?
• Importance and Applications of Large Language Models
• Overview of LLMs in the Context of Natural Language Processing
• Understanding the Architecture of Large Language Models
  • Transformer Architecture
  • Self-Attention Mechanism
  • Pre-training and Fine-tuning of LLMs
• Training and Data Requirements for Large Language Models
  • Training Corpus and Data Collection
  • Pre-processing and Tokenization
  • Training Process and Computational Resources

Prompt Engineering

• What is Prompt Engineering?
• Importance of Prompt Engineering in Modern Organizations
• Role of Managers in Prompt Engineering and Management
• Understanding the Prompt Generation Process
• Design and optimize prompts
  • Apply advanced prompt engineering techniques
  • Review and apply the latest and most advanced prompt engineering techniques
  • Understanding of Multi-modal LLM and different methods in Multi-modal LLMs
  • Tree-of-thought and chain-of-thought methods

Collaborating with Analysts, Engineers, and Scientists

• Generative AI Product Development
  • Building AI first Products
  • Understanding the complexity and challenges
  • Design Exploration and Ideation
  • Simulation and Testing
• Generative AI Project Lifecycle
  • Evaluation metrics for generative AI models
  • Qualitative and quantitative assessment of generative AI outputs
  • User feedback and engagement analysis
  • Continual improvement and iteration techniques
• Data Protection, Privacy and Security
  • Things to consider for protecting Data
  • Data Lifecycle Management
  • Compliances & Regulations
  • Aspects to consider for Data Security
  • Data Privacy Considerations
• Generative AI Deployment
  • Model deployment strategies: on-premises, cloud-based, and edge deployment
  • Integration with existing systems and workflows
  • Testing and performance optimization
  • Monitoring and maintenance of generative AI models
• Responsible AI Considerations
  • Biases
  • Ethical implications of generative AI
  • Fairness, transparency, and accountability in AI projects
  • Regulatory frameworks and guidelines for generative AI
  • Building responsible and ethical generative AI systems
• Understanding the roles and responsibilities of analysts, engineers, and scientists in generative AI projects
• Effective communication and collaboration strategies
• Project scoping and requirement gathering
• Overcoming challenges and mitigating risks in project implementation