Certified Generative AI Engineer (cGenAIe) Certification Program by Tonex
This certification program equips professionals with comprehensive knowledge of generative AI principles, tools, and use cases. It covers model architectures, prompt engineering, risk management, and deployment strategies. Participants gain the skills to design, optimize, and implement generative AI systems across various domains. The course emphasizes ethical use, regulatory awareness, and practical applications. Ideal for engineers and technical professionals aiming to lead AI-driven innovation. This program combines technical depth with real-world relevance to prepare candidates for advanced roles in the AI field.
Audience:
- AI engineers
- Software developers
- Data scientists
- Innovation leads
- System architects
- Technology consultants
Learning Objectives:
- Understand the fundamentals of generative AI
- Explore key generative model types and architectures
- Learn prompt engineering strategies and optimization
- Apply ethical and secure practices in AI deployment
- Design and implement scalable generative AI solutions
Program Modules:
Module 1: Generative AI Foundations
- Evolution of generative models
- Difference between generative and discriminative AI
- AI creativity vs. traditional computation
- Common applications across sectors
- Limitations and challenges
- Introduction to training data needs
Module 2: Architectures and Model Types
- GANs (Generative Adversarial Networks)
- VAEs (Variational Autoencoders)
- Transformers and LLMs
- Diffusion models overview
- Model selection criteria
- Pretraining vs. fine-tuning approaches
Module 3: Prompt Engineering and Optimization
- Principles of prompt design
- Prompt tuning techniques
- Zero-shot and few-shot learning
- Context length and structure control
- Chain-of-thought prompting
- Common pitfalls in prompt design
Module 4: Use Cases and Applications
- Content generation for media
- AI in healthcare and diagnostics
- Legal and policy document automation
- AI for design and creativity
- Enterprise chatbot development
- Personalized learning and tutoring
Module 5: Risks, Ethics, and Governance
- AI hallucinations and misinformation
- Bias detection and mitigation
- IP and copyright issues
- AI misuse and regulatory risks
- Ethical AI frameworks
- Secure model deployment practices
Module 6: Deployment and Lifecycle Management
- Model evaluation and benchmarking
- Resource optimization
- Model monitoring and feedback loops
- Version control and updates
- Integration into enterprise systems
- Performance tuning post-deployment
Exam Domains:
- Core Principles of Generative AI
- Generative Model Architecture and Design
- Prompt Engineering and Input Strategies
- Risk Management and AI Ethics
- Generative AI Application Development
- Governance and Regulatory Frameworks
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of Generative AI. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified Generative AI Engineer (cGenAIe).
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified Generative AI Engineer (cGenAIe) Certification Training exam, candidates must achieve a score of 70% or higher.
Advance your career in AI. Enroll in the cGenAIe program today and become a certified expert in generative AI innovation.