Training plan
Module 1: Plan and prepare to develop AI solutions on Azure
- What is AI?
- Azure AI services
- Azure AI Foundry
- Developer tools and SDKs
- Responsible AI
- Exercise – Prepare for an AI development project
Module 2: Choose and deploy models from the model catalog in Azure AI Foundry portal
- Explore the model catalog
- Deploy a model to an endpoint
- Optimize model performance
- Exercise – Explore, deploy, and chat with language models
Module 3: Develop an AI app with the Azure AI Foundry SDK
- What is the Azure AI Foundry SDK?
- Work with project connections
- Create a chat client
- Exercise – Create a generative AI chat app
Module 4: Get started with prompt flow to develop language model apps in the Azure AI Foundry
- Understand the development lifecycle of a large language model (LLM) app
- Understand core components and explore flow types
- Explore connections and runtimes
- Explore variants and monitoring options
- Exercise – Get started with prompt flow
Module 5: Develop a RAG-based solution with your own data using Azure AI Foundry
- Understand how to ground your language model
- Make your data searchable
- Create a RAG-based client application
- Implement RAG in a prompt flow
- Exercise – Create a generative AI app that uses your own data
Module 6: Fine-tune a language model with Azure AI Foundry
- Understand when to fine-tune a language model
- Prepare your data to fine-tune a chat completion model
- Explore fine-tuning language models in Azure AI Studio
- Exercise – Fine-tune a language model
Module 7: Implement a responsible generative AI solution in Azure AI Foundry
- Plan a responsible generative AI solution
- Map potential harms
- Measure potential harms
- Mitigate potential harms
- Manage a responsible generative AI solution
- Exercise – Apply content filters to prevent the output of harmful content
Module 8: Evaluate generative AI performance in Azure AI Foundry portal
- Assess the model performance
- Manually evaluate the performance of a model
- Automated evaluations
- Exercise – Evaluate generative AI model performance
Recommended prerequisite knowledge
Before starting this learning path, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python.
Azure Generative AI Apps Training
The Azure Generative AI Apps training is designed for AI developers and professionals seeking to master the development of generative AI applications using Azure AI Foundry. This hands-on course covers essential concepts such as designing intelligent conversational applications, configuring generative models, and integrating advanced generative AI capabilities into enterprise solutions.
By participating in this training, professionals will acquire the skills necessary to optimize generative AI application implementations while ensuring responsible AI practices and robust application architecture.
Why Take This Training?
In a world where artificial intelligence is transforming user interactions, developing generative AI applications has become essential for innovation and operational efficiency. Azure AI Foundry offers a comprehensive platform of tools to meet modern enterprise generative needs. This course enables you to understand how to design, develop, and deploy generative AI applications using Azure AI Foundry to ensure exceptional and scalable user experiences.
This training will allow you to integrate advanced generative AI development practices while maximizing engagement and efficiency of your conversational applications.
Skills Developed During Training
Creating and Configuring Azure AI Foundry
Learn to design and configure Azure AI Foundry projects tailored to your organization’s generative needs.Developing Conversational Applications
Discover how to create, configure, and integrate generative AI applications into enterprise environments.Implementing Custom Generative Models
Learn to define and integrate effective language models to enrich the generative capabilities of your applications.Managing Integrations and Connectors
Explore solutions for extending your application functionalities with custom integrations and external connectors.Monitoring and Optimizing Generative Performance
Use Azure native tools to monitor AI application performance and diagnose generative issues.Ensuring Responsible AI Practices
Integrate Azure AI Content Safety, responsible AI frameworks, and governance for ethical generative deployment.
Practical, Results-Oriented Training
Led by certified Azure AI experts, this course provides a balance between theory and practice. Through real-world case studies and interactive exercises, you’ll learn to apply your knowledge to solve complex generative AI application development challenges in Azure.
Who Is This Training For?
- AI developers seeking to enhance their Azure AI Foundry skills
- IT professionals responsible for generative application architecture and implementation
- Cloud specialists managing generative AI infrastructures
- Anyone interested in generative AI application development and responsible AI practices
Optimize Your Generative AI Applications
The Develop Generative AI Applications in Azure (AI-3016) training is ideal for developing your skills in creating conversational applications and cloud-based generative AI. Join us today to learn how to create powerful generative AI applications and ensure reliable and scalable implementations.
Frequently Asked Questions - Azure AI Foundry Development Training (FAQ)
Azure AI Foundry configuration, conversational application development, generative model integration, integration management, and responsible AI implementation.
Basic understanding of Azure services and generative AI concepts is recommended but not mandatory.
Yes, the course features practical labs and real-world scenarios for applying generative AI application development skills.
Azure AI Foundry, Azure OpenAI Service, Azure AI Content Safety, and associated generative development tools.
Yes, the course emphasizes optimizing conversational interactions and improving user engagement.
Absolutely, the training covers enterprise-scale deployment, monitoring, and management of generative AI applications.
