Model-Based GenAI Engineering Workshop by Tonex
This two-day workshop delves into the cutting-edge field of Model-Based GenAI (Generative Artificial Intelligence) Engineering, which combines principles of artificial intelligence (AI) with model-based engineering techniques. Participants will explore how to design, develop, and deploy AI models using a model-based approach, enabling the generation of AI solutions that are efficient, robust, and scalable. Through hands-on exercises, case studies, and interactive sessions, participants will gain insights into harnessing the power of AI through model-based engineering methodologies.
Learning Objectives:
- Understand the fundamentals of Model-Based GenAI Engineering.
- Learn how to develop AI models using model-based engineering techniques.
- Explore various AI generation methods and algorithms.
- Gain hands-on experience in designing and training AI models using model-based approaches.
- Learn how to integrate AI models into real-world applications and systems.
- Understand the potential applications and challenges of Model-Based GenAI Engineering.
Audience:
This workshop is designed for AI engineers, data scientists, software developers, and researchers who are interested in leveraging model-based engineering approaches to develop AI solutions. It is suitable for professionals across various industries, including technology, healthcare, finance, and manufacturing, who are looking to enhance their skills in AI development using innovative methodologies.
Course Modules/Agenda/Outline:
Day 1: Introduction to Model-Based GenAI Engineering
Module 1: Overview of Model-Based GenAI Engineering
- Introduction to Model-Based GenAI Engineering principles and concepts
- Significance of model-based approaches in AI development
Module 2: Model-Based AI Modeling
- Techniques for developing AI models using model-based engineering techniques
- Modeling languages and frameworks for AI development
Module 3: AI Generation Methods and Algorithms
- Overview of various AI generation methods, including neural networks, genetic algorithms, and reinforcement learning
- Hands-on exercises on AI model generation using model-based approaches
Module 4: Case Studies and Best Practices
- Real-world case studies demonstrating the application of Model-Based GenAI Engineering in different domains
- Best practices for developing AI solutions using model-based approaches
Day 2: Practical Applications of Model-Based GenAI Engineering
Module 5: Integration of AI Models into Applications
- Incorporating AI models into real-world applications and systems
- Managing data, scalability, and performance considerations in AI deployment
Module 6: Hands-on Workshop: AI Model Development
- Practical session on designing, training, and evaluating AI models using model-based engineering techniques
- Guided exercises on implementing AI solutions in Python or a similar programming language
Module 7: Challenges and Future Trends in Model-Based GenAI Engineering
- Discussion on challenges and emerging trends in Model-Based GenAI Engineering
- Exploring potential applications and advancements in AI development using model-based approaches
Module 8: Wrap-Up and Q&A
- Recap of key learnings and takeaways from the workshop
- Open forum for questions, discussions, and feedback
Participants will receive comprehensive course materials, including presentation slides, code examples, and hands-on exercises, to support their learning journey throughout the workshop. By the end of the course, participants will have the knowledge and skills to effectively apply Model-Based GenAI Engineering methodologies to develop innovative AI solutions tailored to their specific needs.