Develop generative AI apps in Azure - Applied Skills Workshop Training in Netherlands

This course provides a comprehensive and practical introduction to developing generative AI applications using Azure tools and services.

Throughout the training, participants explore the Azure AI Foundry portal, learn how to evaluate and select suitable models from the model catalog, and deploy those models for use in AI solutions. The course also introduces application development with the Azure AI Foundry SDK and demonstrates how AI capabilities can be incorporated into applications.

The programme focuses on important generative AI development practices, including prompt engineering, Retrieval-Augmented Generation (RAG), working with custom data, model fine-tuning, and application evaluation. Responsible AI is also an important part of the curriculum, helping participants develop generative AI solutions that are not only effective but also designed and deployed with ethical and responsible AI principles in mind.

Participants should have:

• A foundational understanding of Azure services and cloud computing concepts
• Experience working with REST APIs and JSON
• Familiarity with Large Language Models (LLMs) and Natural Language Processing (NLP) concepts
• Basic programming skills, preferably using Python or C#

This course is particularly suitable for:

• AI Engineers
• Solution Architects
• Software Developers
• Technical professionals interested in developing generative AI applications
• Organisations and teams looking to improve service automation and internal productivity through custom AI solutions

By the end of the course, participants will be able to:

• Navigate the Azure AI Foundry portal and understand its main components
• Explore the Azure AI Foundry model catalog and select appropriate models for specific requirements
• Deploy selected models for use in AI solutions
• Develop AI applications using the Azure AI Foundry SDK
• Apply Retrieval-Augmented Generation (RAG) techniques using custom data
• Understand how to fine-tune language models for specific tasks and use cases
• Evaluate and optimise the performance of generative AI applications
• Apply Responsible AI practices when developing and deploying AI solutions

1. Introduction to Azure AI Foundry

• Overview of Azure AI Foundry and its key capabilities
• Understanding the AI development lifecycle within Azure
• Setting up the development environment

2. Model Selection and Deployment

• Exploring the Azure AI Foundry model catalog
• Understanding the criteria for selecting suitable models
• Deploying models through the Azure AI Foundry portal

3. Developing AI Applications with Azure AI Foundry SDK

• Introduction to the Azure AI Foundry SDK
• Developing AI applications using the SDK
• Integrating AI capabilities into existing applications

4. Implementing Retrieval-Augmented Generation (RAG)

• Understanding Retrieval-Augmented Generation and its benefits
• Connecting generative AI solutions to custom data sources
• Creating indexes
• Integrating indexes with generative AI models

5. Fine-Tuning Language Models

• Overview of the model fine-tuning process
• Training models for specific tasks
• Evaluating the performance of fine-tuned models

6. Evaluating and Optimising AI Applications

• Monitoring application performance
• Using Azure tools to evaluate AI applications
• Applying improvements based on evaluation results

7. Responsible AI Practices

• Understanding ethical considerations in AI development
• Implementing measures designed to reduce potential risks
• Supporting compliance with data privacy requirements