AI-102: Designing and Implementing a Microsoft Azure AI Solution
Length: 5 Day(s) Cost:$3650 + GST
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The 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 Develop AI solutions in Azure course will use C# or Python as the programming language.
This course is best suited to Data Scientists and AI Engineers
Before attending this course, students must have:
- Experience in software development, particularly building, managing, and deploying AI solutions
- Knowledge of either C# or Python
- Understanding of REST-based APIs and SDKs for implementing generative AI, computer vision, language analysis, and information extraction on Azure.
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.
After completing this course, students will be able to:
- Describe considerations for AI-enabled application development
- Create, configure, deploy, and secure Azure Cognitive Services
- Develop applications that analyze text
- Develop speech-enabled applications
- Create applications with natural language understanding capabilities
- Create QnA applications
- Create conversational solutions with bots
- Use computer vision services to analyze images and videos
- Create custom computer vision models
- Develop applications that detect, analyze, and recognize faces
- Develop applications that read and process text in images and documents
- Create intelligent search solutions for knowledge mining.
Get started with Azure AI Services
- Plan and prepare to develop AI solutions on Azure
- 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
- Create and consume Azure AI services
- Create Azure AI services resources in an Azure subscription.
- Identify endpoints, keys, and locations required to consume an Azure AI services resource.
- Use a REST API and an SDK to consume Azure AI services.
- Secure Azure AI services
- Consider authentication for Azure AI services
- Manage network security for Azure AI services
- Monitor Azure AI services
- Monitor Azure AI services costs.
- Create alerts and view metrics for Azure AI services.
- Manage Azure AI services diagnostic logging.
- Deploy Azure AI services in containers
- Create containers for reuse
- Deploy to a container and secure a container
- Consume Azure AI services from a container
- Use AI responsibly with Azure AI Foundry Content Safety
- Describe Azure AI Foundry Content Safety
- Describe how Foundry Content Safety operates
- Describe when to use Foundry Content Safety
Develop generative AI apps in Azure
- Plan and prepare to develop AI solutions on Azure
- 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
- Choose and deploy models from the model catalog in Azure AI Foundry portal
- Select a language model from the model catalog.
- Deploy a model to an endpoint.
- Test a model and improve the performance of the model.
- Develop an AI app with the Azure AI Foundry SDK
- 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.
- Get started with prompt flow to develop language model apps in the Azure AI Foundry
- 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.
- Develop a RAG-based solution with your own data using Azure AI Foundry
- 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
- Fine-tune a language model with Azure AI Foundry
- 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.
- Implement a responsible generative AI solution in Azure AI Foundry
- 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
- Evaluate generative AI performance in Azure AI Foundry portal
- Understand model benchmarks.
- Perform manual evaluations.
- Assess your generative AI apps with AI-assisted metrics.
- Configure evaluation flows in the Azure AI Foundry portal.
Develop AI agents on Azure
- Get started with AI agent development on Azure
- Describe core concepts related to AI agents
- Describe options for agent development
- Create and test an agent in the Azure AI Foundry portal
- Develop an AI agent with Azure AI Foundry Agent Service
- 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
- Integrate custom tools into your agent
- 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
- Develop an AI agent with Semantic Kernel
- 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
- Orchestrate a multi-agent solution using Semantic Kernel
- Build AI agents using the Semantic Kernel SDK
- Develop multi-agent solutions
- Create custom selection and termination strategies for agent collaboration
Develop natural language solutions in Azure
- Analyze text with Azure AI Language
- Detect language from text
- Analyze text sentiment
- Extract key phrases, entities, and linked entities
- Create question answering solutions with Azure AI Language
- 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.
- Build a conversational language understanding model
- 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
- Create a custom text classification solution
- 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
- Custom named entity recognition
- Understand tagging entities in extraction projects
- Understand how to build entity recognition projects
- Translate text with Azure AI Translator service
- Provision a Translator resource
- Understand language detection, translation, and transliteration
- Specify translation options
- Define custom translations
- Create speech-enabled apps with Azure AI services
- 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)
- Translate speech with the Azure AI Speech service
- Provision Azure resources for speech translation.
- Generate text translation from speech.
- Synthesize spoken translations.
- Develop an audio-enabled generative AI application
- Deploy an audio-enabled generative AI model in Azure AI Foundry.
- Create a chat app that submits audio-based prompts.
Develop computer vision solutions in Azure
- Analyze images
- Provision an Azure AI Vision resource.
- Use the Azure AI Vision SDK to connect to your resource.
- Write code to analyze an image.
- Read text in images
- 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.
- Detect, analyze, and recognize faces
- 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.
- Classify images
- 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
- Detect objects in images
- 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
- Analyze video
- Describe Azure Video Indexer capabilities
- Extract custom insights
- Use Azure Video Indexer widgets and APIs
- Develop a vision-enabled generative AI application
- 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.
- Generate images with AI
- Describe the capabilities of image generation models
- Use the Images playground in Azure AI Foundry portal
- Integrate image generation models into your apps