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Generative AI for Technical Managers

Ramping up Managers to lead and manage Generative AI Projects in an effective way!


2 Days


Basic Level

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Generative AI has emerged as a powerful tool for creating new and innovative solutions in various industries. This course is designed to provide technical managers with a comprehensive understanding of generative AI techniques and their applications. Participants will gain insights into the underlying principles, practical implementation, and management considerations associated with generative AI projects. Through a combination of theoretical lectures, case studies, and hands-on exercises, participants will develop the necessary knowledge and skills to effectively lead generative AI initiatives within their organizations.

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The “Generative AI for Technical Managers” course is designed specifically for technical managers who are looking to enhance their knowledge and skills in the field of generative AI.

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    • 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
  • 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
  • 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
  • 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
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  • Basic understanding of artificial intelligence concepts and technologies.
  • Familiarity with programming fundamentals and basic coding experience.
  • Prior experience in a technical or managerial role.


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