GenAI Developer Certification for Professionals

Overview of Generative AI and its applications

Difference between traditional AI and Generative AI

Types of Generative AI models (GPT, DALL?E, Stable Diffusion, etc.)

Ethical considerations in Generative AI

AI governance and responsible AI principles

Neural networks and their role in Generative AI

Understanding backpropagation and optimization techniques

Common deep learning architectures (CNNs, RNNs, Transformers)

Introduction to PyTorch and TensorFlow for AI development

Understanding Transformer-based models (BERT, GPT, T5, etc.)

Fine-tuning LLMs for specific applications

Optimizing and deploying LLMs efficiently

Evaluating performance and mitigating biases in LLMs

Understanding diffusion models and GANs

Generating images using models like DALL?E and Stable Diffusion

Deepfake technology and ethical implications

Optimizing image generation for quality and speed

Understanding multimodal AI and cross-domain learning

Combining text and image generation using CLIP and similar models

Text-to-Speech (TTS) and AI voice synthesis

Evaluating multimodal AI models

Developing AI-powered chatbots and virtual assistants

Integrating Generative AI into business applications

Deploying AI models on cloud platforms (Azure, AWS, GCP)

Optimizing performance and cost-efficiency in AI applications

Addressing AI security risks and adversarial attacks

Legal and regulatory considerations in AI development

The future of Generative AI and emerging trends

Best practices for responsible AI development

Exam structure and key focus areas

Practice questions and mock tests

Hands-on labs and real-world projects

AI leadership best practices