Overview
Generative Artificial Intelligence (AI) is becoming more accessible through comprehensive development platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models to chat with your users.
As of 13th June, the AI-102 Develop AI Solutions in Azure course has been updated and restructured by combining together these five 1-day AI courses into a single, comprehensive learning experience:
- AI-3016: Develop generative AI apps in Azure
- AI-3026: Develop AI Agents on Azure
- AI-3002: Develop AI information extraction solutions in Azure
- AI-3003: Build a natural language processing solution with Azure AI Services
- AI-3004: Build an Azure AI Vision solution with Azure AI services
This update provides learners with a more cohesive and in-depth journey through key AI concepts and applications.
Skills Covered
- Plan and prepare to develop AI solutions on Azure
- Choose and deploy models from the model catalog in Azure AI Foundry portal
- Develop an AI app with the Azure AI Foundry SDK
- Get started with prompt flow to develop language model apps in the Azure AI Foundry
- Develop a RAG-based solution with your own data using Azure AI Foundry
- Fine-tune a language model with Azure AI Foundry
- Implement a responsible generative AI solution in Azure AI Foundry
- Evaluate generative AI performance in Azure AI Foundry portal
Who Should Attend
- Data Scientist
- AI Engineer
Course Curriculum
Prerequisites
- Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. You should also be proficient in programming with Python or Microsoft C#.
Download Course Syllabus
Course Modules
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.
Learning objectives
By the end of this module, you’ll be able to:
- Identify common AI capabilities that you can implement in applications
- Describe Azure AI Services and considerations for using them
- Describe Azure AI Foundry and considerations for using it
- Identify appropriate developer tools and SDKs for an AI project
- Describe considerations for responsible AI
Prerequisites
Before starting this module, you should be familiar with:
- Basic software development concepts
- Basic AI concepts
- Basic Azure concepts
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.
Learning objectives
By the end of this module, you’ll be able to:
- Select a language model from the model catalog.
- Deploy a model to an endpoint.
- Test a model and improve the performance of the model.
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.
Use the Azure AI Foundry SDK to develop AI applications with Azure AI Foundry projects.
Learning objectives
After completing this module, you’ll be able to:
- Describe capabilities of the Azure AI Foundry SDK.
- Use the Azure AI Foundry SDK to work with connections in projects.
- Use the Azure AI Foundry SDK to develop an AI chat app.
Prerequisites
Before starting this module, you should:
- Be familiar with Azure services and the Azure portal.
- Have some programming experience with Python or C#.
Learn about how to use prompt flow to develop applications that leverage language models in the Azure AI Foundry.
Learning objectives
By the end of this module, you’ll be able to:
- Understand the development lifecycle when creating language model applications.
- Understand what a flow is in prompt flow.
- Explore the core components when working with prompt flow.
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.
Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Azure AI Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.
Learning objectives
By the end of this module, you’ll be able to:
- Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
- Index your data with Azure AI Search to make it searchable for language models
- Build an agent using RAG on your own data in the Azure AI Foundry portal
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure.
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.
Learning objectives
By the end of this module, you’ll be able to:
- Understand when to fine-tune a model.
- Prepare your data to fine-tune a chat completion model.
- Fine-tune a base model in the Azure AI Foundry portal.
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.
Learning objectives
By the end of this module, you’ll be able to:
- Describe an overall process for responsible generative AI solution development
- Identify and prioritize potential harms relevant to a generative AI solution
- Measure the presence of harms in a generative AI solution
- Mitigate harms in a generative AI solution
- Prepare to deploy and operate a generative AI solution responsibly
Prerequisites
Before starting this module, you should be familiar with Azure AI Foundry. Consider completing the Introduction to Azure AI Foundry module before starting this one.
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.
Learning objectives
By the end of this module, you’ll be able to:
- Understand model benchmarks.
- Perform manual evaluations.
- Assess your generative AI apps with AI-assisted metrics.
- Configure evaluation flows in the Azure AI Foundry portal.
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.
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Training Options
- ILT: Instructor-Led Training
- VILT: Virtual Instructor-Led Training
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Exam & Certification
This course is not associated with any Certification.
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