Fundamentals of generative AI and its applications:

This involves understanding the basic principles behind generative artificial intelligence (AI), which focuses on creating new data rather than simply analyzing existing data. It includes learning about various algorithms and techniques used in generative AI and exploring its applications across different domains such as art generation, data augmentation, and content creation.

Explore Azure's generative AI services and tools:

This entails familiarizing oneself with the generative AI services and tools offered by Microsoft Azure, a cloud computing platform. This may include services like Azure Machine Learning, Azure Cognitive Services, and Azure AI Platform, which provide capabilities for building, training, and deploying generative AI models.

Training and deploying generative models on Azure:

This involves hands-on experience in training and deploying generative models using Azure's infrastructure and services. It includes tasks such as data preparation, model training, hyperparameter tuning, and deploying models to production environments on Azure.

Proficiency in generating Azure services:

This requires the ability to generate synthetic data, images, and text using generative AI techniques and Azure services. It involves understanding how to use these tools to create realistic and diverse synthetic data that can be used for various purposes such as training machine learning models and testing applications.