Introduction to Azure AI Foundry
- Overview of Azure AI Foundry and its capabilities
- Understanding the AI development lifecycle in Azure
- Setting up the development environment
Model selection and deployment
- Exploring the Azure AI Foundry model catalogue
- Criteria for selecting appropriate models
- Deploying models using the Azure AI Foundry portal
Developing AI applications with Azure AI Foundry SDK
- Introduction to the Azure AI Foundry SDK
- Building AI applications using the SDK
- Integrating AI capabilities into existing applications
Implementing Retrieval-Augmented Generation (RAG)
- Understanding RAG and its benefits
- Connecting to custom data sources
- Creating indexes and integrating them with generative AI models
Fine-tuning language models
- Overview of model fine-tuning processes
- Training models for specific tasks
- Evaluating fine-tuned model performance
Evaluating and optimizing AI applications
- Monitoring application performance
- Using Azure tools for evaluation
- Implementing improvements based on evaluation results
Responsible AI practices
- Understanding ethical considerations in AI development
- Implementing measures to mitigate risks
- Ensuring compliance with data privacy regulations
Exams and assessments
There are no formal exams included in this course. Learners will complete interactive labs, guided exercises, and scenario-based tasks to reinforce understanding and assess their progress.
Hands-on learning
This course includes:
- Guided labs on model deployment, application development, and RAG implementation
- Practical exercises for fine-tuning models and evaluating performance
- Simulated real-world scenarios for applying responsible AI practices
- Instructor feedback and collaborative learning activities
