Develop AI Solutions in Azure – Build, Deploy, and Scale Intelligent Apps with Microsoft’s Most Advanced AI Stack
Unlock the full potential of generative AI, language models, vision APIs, and intelligent agents—all inside the Microsoft Azure ecosystem. The Develop AI Solutions in Azure course is an intensive 5-day, hands-on program designed for software developers who want to create smart applications using the Azure AI Foundry, Semantic Kernel, and other powerful services
Training Duration: 5 Days
- Certificate Of Completion Available
- Group Private Class
- VILT Class Available
- SBL-Khas Claimable
DevOps is a set of best practices that emphasizes collaboration and AI-102: Develop AI solutions in Azure is intended for software developers wanting to build AI infused applications that leverage Azure AI Foundry and other Azure AI services. Topics in this course include developing generative AI apps, building AI agents, and solutions that implement computer vision and information extraction. The course will use C# or Python as the programming language.
Before attending this course, students must have:
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Knowledge of Microsoft Azure and ability to navigate the Azure portal
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Knowledge of either C# or Python
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Familiarity with JSON and REST programming semantics
To gain C# or Python skills, complete the free Take your first steps with C# or Take your first steps with Python learning path before attending the course.
If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.
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This course was designed for software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Foundry and other Azure AI services. They are familiar with C# or Python and have knowledge on using REST-based APIs and SDKs to build generative AI, computer vision, language analysis, and information extraction solutions on Azure.
Module 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.
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
Module 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.
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.
Module 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.
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#.
Module 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.
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.
Module 5: Develop a RAG-based solution with your own data using Azure AI Foundry
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.
Module 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.
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.
Module 7: Implement a responsible generative AI solution in Azure AI Foundry
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.
Module 8: Evaluate generative AI performance in Azure AI Foundry portal
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.
Module 9: Get started with AI agent development on Azure
AI agents represent the next generation of intelligent applications. Learn how they can be developed and used on Microsoft Azure.
Learning objectives
By the end of this module, you'll be able to:- Describe core concepts related to AI agents
- Describe options for agent development
- Create and test an agent in the Azure AI Foundry portal
Prerequisites
- Before starting this module, you should be familiar with fundamental AI concepts and services in Azure.
Module 10: Develop an AI agent with Azure AI Foundry Agent Service
This module provides engineers with the skills to begin building agents with Azure AI Foundry Agent Service.
Learning objectives
By the end of this module, you'll be able to:- Describe the purpose of AI agents
- Explain the key features of Azure AI Foundry Agent Service
- Build an agent using the Foundry Agent Service
- Integrate an agent in the Foundry Agent Service into your own application
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Fundamentals of Generative AI.
Module 11: Integrate custom tools into your agent
Built-in tools are useful, but they may not meet all your needs. In this module, learn how to extend the capabilities of your agent by integrating custom tools for your agent to use.
Learning objectives
By the end of this module, you'll be able to:- Describe the benefits of using custom tools with your agent.
- Explore the different options for custom tools.
- Build an agent that integrates custom tools using the Azure AI Foundry Agent Service.
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Fundamentals of Generative AI.
- It's highly recommended you have experience with the Azure AI Foundry Agent Service. You can learn more with Develop an AI agent with Azure AI Foundry Agent Service.
Module 12: Develop an AI agent with Semantic Kernel
This module provides engineers with the skills to begin building Azure AI Foundry Agent Service agents with Semantic Kernel.
Learning objectives
By the end of this module, you'll be able to:- Use Semantic Kernel to connect to an Azure AI Foundry project
- Create Azure AI Foundry Agent Service agents using the Semantic Kernel SDK
- Integrate plugin functions with your AI agent
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Fundamentals of Generative AI.
Module 13: Orchestrate a multi-agent solution using Semantic Kernel
Learn how to use the Semantic Kernel SDK to develop your own AI agents that can collaborate for a multi-agent solution.
Learning objectives
By the end of this module, you'll be able to:- Build AI agents using the Semantic Kernel SDK
- Develop multi-agent solutions
- Create custom selection and termination strategies for agent collaboration
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Fundamentals of Generative AI.
Module 14: Analyze text with Azure AI Language
The Azure AI Language service enables you to create intelligent apps and services that extract semantic information from text.
Learning objectives
In this module, you'll learn how to use the Azure AI Language service to:- Detect language from text
- Analyze text sentiment
- Extract key phrases, entities, and linked entities
Prerequisites
- Before starting this module, you'll need
- Familiarity with Microsoft Azure and the Azure portal.
- Experience programming with C# or Python.
Module 15: Create question answering solutions with Azure AI Language
The question answering capability of the Azure AI Language service makes it easy to build applications in which users ask questions using natural language and receive appropriate answers.
Learning objectives
After completing this module, you will be able to:- Understand question answering and how it compares to language understanding.
- Create, test, publish, and consume a knowledge base.
- Implement multi-turn conversation and active learning.
- Create a question answering bot to interact with using natural language.
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
Module 16: Build a conversational language understanding model
The Azure AI Language conversational language understanding service (CLU) enables you to train a model that apps can use to extract meaning from natural language.
Learning objectives
After completing this module, you'll be able to:- Provision Azure resources for Azure AI Language resource
- Define intents, utterances, and entities
- Use patterns to differentiate similar utterances
- Use pre-built entity components
- Train, test, publish, and review an Azure AI Language model
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
Module 17: Create a custom text classification solution
The Azure AI Language service enables processing of natural language to use in your own app. Learn how to build a custom text classification project.
Learning objectives
After completing this module, you'll be able to:- Understand types of classification projects
- Build a custom text classification project
- Tag data, train, and deploy a model
- Submit classification tasks from your own app
Prerequisites
Before starting this module, you should be familiar with:- The Azure portal
- Familiarity with Azure AI Services
- General programming techniques
Module 18: Custom named entity recognition
Build a custom entity recognition solution to extract entities from unstructured documents
Learning objectives
After completing this module, you'll be able to:- Understand tagging entities in extraction projects
- Understand how to build entity recognition projects
Prerequisites
Before starting this module, you should be familiar with:- The Azure portal
- General functionality of Azure AI Services
- General programming technique
Module 19: Translate text with Azure AI Translator service
The Translator service enables you to create intelligent apps and services that can translate text between languages.
Learning objectives
After completing this module, you'll be able to:- Provision a Translator resource
- Understand language detection, translation, and transliteration
- Specify translation options
- Define custom translations
Prerequisites
Before starting this module, you need- Familiarity with Microsoft Azure and the Azure portal.
- Experience programming with C# or Python.
Module 20: Create speech-enabled apps with Azure AI services
The Azure AI Speech service enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.
Learning objectives
In this module, you'll learn how to:- Provision an Azure resource for the Azure AI Speech service
- Implement speech recognition with the Azure AI Speech to text API
- Use the Text to speech API to implement speech synthesis
- Configure audio format and voices
- Use Speech Synthesis Markup Language (SSML)
Prerequisites
Before starting this module, you should:- Be familiar with Azure services and the Azure portal
- Have experience programming with C# or Python
Module 21: Translate speech with the Azure AI Speech service
Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language and returning translations of the transcription in one or more other languages.
Learning objectives
In this module, you will learn how to:- Provision Azure resources for speech translation.
- Generate text translation from speech.
- Synthesize spoken translations.
Prerequisites
Before starting this module, you should:- Be familiar with Azure services and the Azure portal.
- Have experience programming with C# or Python.
- Have experience of using the Azure AI Speech service to transcribe speech to text.
Module 22: Develop an audio-enabled generative AI application
A voice carries meaning beyond words, and audio-enabled generative AI models can interpret spoken input to understand tone, intent, and language. Learn how to build audio-enabled chat apps that listen and respond to audio.
Learning objectives
After completing this module, you'll be able to:- Deploy an audio-enabled generative AI model in Azure AI Foundry.
- Create a chat app that submits audio-based prompts.
Prerequisites
Before starting this module, you should have:- Experience with deploying generative AI models in Azure AI Foundry.
- Programming experience with Python or Microsoft C#.
Module 23: Analyze images
With the Azure AI Vision service, you can use pre-trained models to analyze images and extract insights and information from them.
Learning objectives
After completing this module, you’ll be able to:- Provision an Azure AI Vision resource.
- Use the Azure AI Vision SDK to connect to your resource.
- Write code to analyze an image.
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python
Module 24: Read text in images
The Azure AI Vision Image Analysis service uses algorithms to process images and return information. This module teaches you how to use the Image Analysis API for optical character recognition (OCR).
Learning objectives
In this module, you'll learn how to:- Describe the OCR capabilities of Azure AI Vision's Image Analysis API.
- Use the Azure AI Vision service Image Analysis API to extract text from images.
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python.
Module 25: Detect, analyze, and recognize faces
The ability for applications to detect human faces, analyze facial features and emotions, and identify individuals is a key artificial intelligence capability.
Learning objectives
After completing this module, you'll be able to:- Describe the capabilities of the Azure AI Vision Face service.
- Write code to detect and analyze faces in an image.
- Describe facial recognition support in Azure AI Vision Face.
- Describe responsible AI considerations when developing facial solutions.
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python.
Module 26: Classify images
Image classification is used to determine the main subject of an image. You can use the Azure AI Custom Vision services to train a model that classifies images based on your own categorizations.
Learning objectives
After completing this module, you'll be able to:- Provision Azure resources for Azure AI Custom Vision
- Train an image classification model
- Use the Azure AI Custom Vision SDK to create an image classification client application
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python.
Module 27: Detect objects in images
Object detection is used to locate and identify objects in images. You can use Azure AI Custom Vision to train a model to detect specific classes of object in images.
Learning objectives
After completing this module, you'll be able to:- Provision Azure resources for Azure AI Custom Vision
- Understand object detection
- Train an object detector
- Use the Azure AI Custom Vision SDK to create an object detection client application
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python.
Module 28: Analyze video
Azure Video Indexer is a service to extract insights from video, including face identification, text recognition, object labels, scene segmentations, and more.
Learning objectives
After completing this module, you'll be able to:- Describe Azure Video Indexer capabilities
- Extract custom insights
- Use Azure Video Indexer widgets and APIs
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
Module 29: Develop a vision-enabled generative AI application
A picture says a thousand words, and multimodal generative AI models can interpret images to respond to visual prompts. Learn how to build vision-enabled chat apps.
Learning objectives
After completing this module, you'll be able to:- Deploy a vision-enabled generative AI model in Azure AI Foundry.
- Test an image-based prompt in the chat playground.
- Create a chat app that submits image-based prompts.
Prerequisites
Before starting this module, you should have:- Experience with deploying generative AI models in Azure AI Foundry.
- Programming experience with Python or Microsoft C#.
Module 30: Generate images with AI
In Azure AI Foundry, you can use image generation models to create original images based on natural language prompts.
Learning objectives
After completing this module, you'll be able to:- Describe the capabilities of image generation models
- Use the Images playground in Azure AI Foundry portal
- Integrate image generation models into your apps
Prerequisites
- Before starting this module, you should be familiar with Azure AI Foundry. You should also have programming experience with Python or Microsoft C#.
Module 31: Create a multimodal analysis solution with Azure AI Content Understanding
Use Azure AI Content Understanding for multimodal content analysis and information extraction.
Learning objectives
After completing this module, you will be able to:- Describe capabilities of Azure AI Content Understanding.
- Use Azure AI Content Understanding to build a content analyzer.
- Consume a Content Understanding analyzer by using the REST API.
Prerequisites
Before starting this module, you should:- Be familiar with Azure services and the Azure portal.
- Have some familiarity with REST APIs.
Module 32: Create an Azure AI Content Understanding client application
Use the Azure AI Content Understanding REST API for multimodal content analysis and information extraction.
Learning objectives
After completing this module, you will be able to:- Use the Azure AI Content Understanding REST API to build a content analyzer.
- Use the Azure AI Content Understanding REST API to consume an analyzer.
Prerequisites
Before starting this module, you should:- Be familiar with Azure services and the Azure portal.
- Have some familiarity with Python and REST APIs.
Module 33: Use prebuilt Document intelligence models
Learn what data you can analyze by choosing prebuilt Forms Analyzer models and how to deploy these models in a Document intelligence solution.
Learning objectives
In this module, you'll learn to:- Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
- Analyze forms by using the General Document, Read, and Layout models.
- Analyze forms by using financial, ID, and tax prebuilt models.
Prerequisites
- Basic understanding of Document intelligence
Module 34: Extract data from forms with Azure Document intelligence
Document intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document intelligence cognitive service.
Learning objectives
In this module, you learn how to:- Identify how Document intelligence's layout service, prebuilt models, and custom models can automate processes.
- Use Document intelligence's capabilities with SDKs, REST API, and Document Intelligence Studio.
- Develop and test custom models.
Prerequisites
To complete this module, you'll need:- An active Azure account
- Knowledge of Azure portal navigation
- Knowledge of at least one programming language (C#, Python)
Module 35: Create a knowledge mining solution with Azure AI Search
Unlock the hidden insights in your data with Azure AI Search. In this module, you'll learn how to implement a knowledge mining solution that extracts and enriches data, making it searchable and ready for deeper analysis.
Learning objectives
After completing this module, you'll be able to:- Implement indexing with Azure AI Search
- Use AI skills to enrich data in an index
- Search an index to find relevant information
- Persist extracted information in a knowledge store
Prerequisites
Before starting this module, you should already have:- Familiarity with Azure
- Some knowledge of AI concepts