Training Contents 2024
Training content for 2024
Table of contents
- Learning Path 1: Introduction to AI and AI on Azure
- Learning Path 2: Develop computer vision solutions with Azure AI Vision
- Learning Path 3: Develop natural language processing solutions
- Learning Path 4: Develop Generative AI Solutions with Azure OpenAI Service
- Learning Path 5: Creating a Knowledge Mining Solution
- Learning Path 6: Develop solutions with Azure AI Document Intelligence
Learning Path 1: Introduction to AI and AI on Azure
In this Learning Path, you’ll learn about:
- Described artificial intelligence and how it compares to machine learning and data science.
- Described Azure AI services.
- Understood how to get started with Azure AI services
- Understood how to use Azure AI Services for enterprise applications
| Module | Learning Objectives |
|---|---|
| Introduction to AI and Azure AI services | - Describe artificial intelligence and how it compares to machine learning and data science. - Describe Azure AI services. |
| Get started with Azure AI services | - Understand Azure AI APIs. - Create and consume Azure AI services resources. |
| Using Azure AI Services for enterprise applications | - Consider and manage authentication and network security for Azure AI services. - Manage costs, view metrics, and manage alerts and diagnostic logging. - Deploy to secure containers and consume Azure AI services from containers. |
| Lab | Exercise | Estimated time |
|---|---|---|
| Get Started with Azure AI Services | - Clone the repository in Visual Studio Code - Provision an Azure AI Services resource - Use a REST Interface - Use an SDK | 30 minutes |
Learning Path 2: Develop computer vision solutions with Azure AI Vision
In this Learning Path, you’ll learn about:
- Used Image Analysis to analyze images, extract insights, remove background, and perform OCR.
- Detected faces and facial recognition.
- Created custom vision models trained on your own images.
- Extracted insights from videos with Azure Video Indexer.
| Module | Learning Objectives |
|---|---|
| Analyze and manipulate images | - Understand features and functionality of Image Analysis - Perform Optical Character Recognition (OCR) - Connect an app to Image Analysis APIs |
| Detecting Faces with the Azure AI Vision | - Understand features, use cases, and responsibility of the Azure AI Vision Face API - Use the Face API in an app |
| Custom vision models with Azure AI Vision | - Understand use cases of custom Vision models - Label data in Azure ML for both image classification and object detection - - Use a custom vision model in an app |
| Analyze video | - Azure Video Indexer capabilities - Custom Insights - Video Indexer Widgets and API |
| Lab | Exercise | Estimated time |
|---|---|---|
| Analyze Images with Azure AI Vision | -Clone the repository for this course - Provision an Azure AI Services resource - Prepare to use the Azure AI Vision SDK - View the images you will analyze - Analyze an image to suggest a caption - Get suggested tags for an image - Detect and locate objects in an image - Detect and locate people in an image - Remove the background or generate a foreground matte of an image | No information |
| Read Text in Images | - Clone the repository for this course - Provision an Azure AI Services resource - Prepare to use the Azure AI Vision SDK - Use the Azure AI Vision SDK to read text from an image - Use the Azure AI Vision SDK to read handwritten text from an image | No information |
| Classify images with an Azure AI Vision custom model | -Clone the repository for this course - Provision Azure resources - Create a custom model training project - Test your custom model | No information |
Learning Path 3: Develop natural language processing solutions
In this Learning Path, you’ll learn about:
- Analyze and translate text.
- Build a conversational language understanding model.
- Build a question answering solution
- Speech recognition, synthesis, and translation.
- Connect an app to Azure AI Language resources.
| Module | Learning Objectives |
|---|---|
| Analyzing text | - Detect language and extract key phrases - Analyze sentiment and detect PII - Summarize text - Extract entities and linked entities |
| Translating text | - Translate text |
| Build a question answering solution | - Describe the question answering capabilities of Azure AI Language. - Describe the differences between question answering and conversational language understanding. - Create a knowledge base. - Implement multi-turn conversation. - Test and publish a knowledge base. - Consume a published knowledge base. - Implement active learning. |
| Build a conversational language understanding app | - Provision an Azure AI Language resource - Define intents, entities, and utterances - Use patterns to differentiate similar utterances and use pre-built entity components - Train, test, publish, and review a model - Describe Azure AI Language Understanding features |
| Custom classification and named entity extraction | - Label documents, train and deploy models for custom classification - Understand model performance and see where to improve your model - Use your custom model in an app |
| Speech recognition, translation and synthesis | - Provision an Azure resource for the Azure AI Speech service - Use the Speech to text API to implement speech recognition - Use the Text to speech API to implement speech synthesis - Configure audio format and voices - Use Speech Synthesis Markup Language (SSML) |
| Lab | Exercise | Estimated time |
|---|---|---|
| Analyze Text | - Provision an Azure AI Language resource - Prepare to develop an app in Visual Studio Code - Configure your application - Add code to detect language - Add code to evaluate sentiment - Add code to identify key phrases - Add code to extract entities - Add code to extract linked entities | No information |
| Create a Question Answering solution | - Provision an Azure AI Language resource - Create a question answering project - Add sources to the knowledge base - Edit the knowledge base - Train and test the knowledge base - Deploy the knowledge base - Prepare to develop an app in Visual Studio Code - Configure your application - Add code to the application | No information |
| Create a conversational language understanding app | - Provision an Azure AI Language resource - Create a conversational language understanding project - Add entities - Use the model from a client app | No information |
| - Recognize and Synthesize Speech | - Provision an Azure AI Speech resource - Prepare to develop an app in Visual Studio Code - Configure your application - Add code to use the Azure AI Speech SDK - Add code to recognize speech - Synthesize speech - Use a different voice - Use Speech Synthesis Markup Language | No information |
Learning Path 4: Develop Generative AI Solutions with Azure OpenAI Service
In this Learning Path, you’ll learn about:
- Created and deployed Azure OpenAI resources.
- Integrated Azure OpenAI into your application through REST APIs and SDKs.
- Explored prompt engineering techniques to improve model responses.
- Connected your own data for grounding an Azure OpenAI model.
| Module | Learning Objectives |
|---|---|
| Get started with Azure OpenAI Service | - Describe what generative AI is - Provision a resource and deploy a model - Use Azure AI Studio |
| Develop apps with Azure OpenAI Service | - Integrate Azure OpenAI into your app - Use the REST API - Use language specific SDKs |
| Apply prompt engineering with Azure OpenAI Service | - Understand what prompt engineering is - Understand considerations for different endpoints - Explore different techniques of prompt engineering |
| Use your own data with Azure OpenAI Service | - Understand how RAG using your own data works - Use the REST API - Use language specific SDKs |
| Lab | Exercise | Estimated time |
|---|---|---|
| Integrate Azure OpenAI into your app | - Provision an Azure OpenAI resource - Deploy a model - Prepare to develop an app in Visual Studio Code - Configure your application - Add code to use the Azure - OpenAI service - Test your application - Maintain conversation history | 30 minutes |
| Utilize prompt engineering in your app | - Provision an Azure - OpenAI resource - Deploy a model - Explore prompt engineering techniques - Prepare to develop an app in Visual Studio Code - Configure your application - Add code to use the Azure OpenAI service - Run your application | 30 minutes |
| Add your data for RAG with Azure OpenAI Service | -Provision an Azure OpenAI resource - Deploy a model - Observe normal chat behavior without adding your own data - Connect your data in the chat playground - Chat with a model grounded in your data - Connect your app to your own data - Configure your application - Run your application | 30 minutes |
Learning Path 5: Creating a Knowledge Mining Solution
In this Learning Path, you’ll learn about:
- Create an Azure AI Search Solution.
- Implement a custom skill for Azure AI Search and integrate it into a skillset.
- Create a knowledge store with object, file, and table projections.
| Module | Learning Objectives |
|---|---|
| Implementing an Intelligent Search Solution | - Create an Azure AI Search Solution |
| Create a custom skill for Azure AI Search | - Implement a custom skill for Azure AI Search and integrate it into a skillset |
| Creating a Knowledge Store | - Create a knowledge store with object, file, and table projections |
| Lab | Exercise | Estimated time |
|---|---|---|
| Create a Custom Skill for Azure AI Search | - Prepare to develop an app in Visual Studio Code - Create Azure resources - Create a search solution - Search the index - Create an Azure Function for a custom skill - Add the custom skill to the search solution - Search the index | No information |
Learning Path 6: Develop solutions with Azure AI Document Intelligence
In this Learning Path, you’ll learn about:
- Explored available prebuilt models, and how to use them in Document Intelligence Studio.
- Trained and deployed a custom model.
- Connected an app to use Document Intelligence APIs.
| Module | Learning Objectives |
|---|---|
| Use prebuilt Document Intelligence models | - Understand models in Azure AI Document Intelligence |
| Train a custom Document Intelligence model | - Train a custom Document Intelligence model - Connect an app to Document Intelligence APIs |
| Lab | Exercise | Estimated time |
|---|---|---|
| Extract Data from Forms | - Prepare to develop an app in Visual Studio Code - Create a Azure AI Document Intelligence resource - Gather documents for training - Train the model using - Document Intelligence Studio - Test your custom Document Intelligence model | No information |