Generative AI for Technical Managers
Ramping up Managers to lead and manage Generative AI Projects in an effective way.
Type
PaidDecember 20 @ 9:00 am – December 21 @ 5:00 pm UTC+5:30
About the event
Are you a technical manager looking to harness the power of Generative AI? Look no further! Join us for an exclusive workshop designed to equip you with the knowledge and skills to lead and manage Generative AI projects effectively.
Unleash the Potential of Generative AI!
This immersive course will delve deep into the world of generative AI, providing you with a comprehensive understanding of its principles, applications, and practical implementation. You’ll learn how to identify opportunities, build high-performing teams, and drive innovation through generative AI.
Key Takeaways:
- Master the fundamentals of generative AI and its impact on industries.
- Learn how to identify and evaluate generative AI projects.
- Develop strategies for building and leading high-performing generative AI teams.
- Understand the ethical and legal considerations of generative AI.
- Gain hands-on experience through practical exercises and case studies.
Who Should Attend: This workshop is tailored for technical managers who want to stay ahead of the curve. If you’re eager to transform your organization with generative AI, this is your chance!
Prerequisites:
- Basic understanding of artificial intelligence concepts
- Familiarity with programming fundamentals
- Prior experience in a technical or managerial role
Don’t miss this opportunity to become a Generative AI leader! Register now and secure your spot.
- 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
- Applications of Generative AI and Deep Learning
- 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
