Develop Generative AI Apps in Azure – Intensive Training («AI316»)

• Identifying common AI capabilities and implementing them in applications
• Understanding Azure AI Services and Azure AI Foundry, including considerations for their usage
• Selecting, deploying, and testing language models, improving their performance
• Utilizing the Azure AI Foundry SDK for various tasks, including working with connections and building chat apps
• Grasping the development lifecycle for language model applications, including prompt flow
• Implementing Retrieval Augmented Generation (RAG) to ground language models and use Azure AI Search for data indexing
• Understanding when and how to fine-tune models
• Evaluating and assessing generative AI applications using benchmarks, manual evaluations, and AI-assisted metrics, with a focus on responsible AI development and deployment

1 Plan and prepare to develop AI solutions on Azure
Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team.

2 Choose and deploy models from the model catalog in Azure AI Foundry portal
Choose the various language models that are available through the Azure AI Foundry's model catalog. Understand how to select, deploy, and test a model, and to improve its performance.

3 Develop an AI app with the Azure AI Foundry SDK
Use the Azure AI Foundry SDK to develop AI applications with Azure AI Foundry projects.

4 Get started with prompt flow to develop language model apps in the Azure AI Foundry
Learn about how to use prompt flow to develop applications that leverage language models in the Azure AI Foundry.

5 Build a RAG-based agent with your own data using Azure AI Foundry
Agents can work alongside you to provide suggestions, generate content, or help you make decisions. Agents use language models as a form of generative artificial intelligence (AI) and will answer your questions using the data they were trained on. To ensure an agent retrieves information from a specific source, you can add your own data when building an agent with the Azure AI Foundry.

6 Fine-tune a language model with Azure AI Foundry
Train a base language model on a chat-completion task. The model catalog in Azure AI Foundry offers many open-source models that can be fine-tuned for your specific model behavior needs.

7 Evaluate the performance of generative AI apps with Azure AI Foundry
Evaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio.

8 Responsible generative AI
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.

The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions and want to create, customize, and deploy their own Copilots.

• Basic software development concepts
• Basic AI concepts
• Basic Azure concepts
• Familiarity with Azure services and the Azure portal
• Basic programming experience with Python or C#