AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. The course will use C# or Python as the programming language.
After completing this course, students will be able to:
- Understand AI Solution Requirements
- Design AI Solutions
- Build and Train AI Models
- Deploy AI Models
- Integrate AI Models into Applications
- Monitor and Maintain AI Solutions
- Work with Cognitive Services
- Implement Natural Language Processing (NLP) Solutions
- Build Conversational AI Solutions
- Understand Responsible AI Practices
- Security and Compliance in AI Solutions
Audience Profile
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.
Before attending this course, students must have:
If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing Microsoft Azure AI Fundamentals Training (AI-900) before taking this one.
You should already have:
- Knowledge of Microsoft Azure and ability to navigate the Azure portal
- Knowledge of either C# or Python
- Familiarity with JSON and REST programming semantics
- Prepare to develop AI solutions on Azure
- Define artificial intelligence
- Understand AI-related terms
- Understand considerations for AI Engineers
- Understand considerations for responsible AI
- Understand capabilities of Azure Machine Learning
- Understand capabilities of Azure AI Services
- Understand capabilities of the Azure Bot Service
- Understand capabilities of Azure Cognitive Search
- Create and consume Azure AI services
- Provision an Azure AI services resource
- Identify endpoints and keys
- Use a REST API
- Use an SDK
- Secure Azure AI services
- Consider authentication
- Implement network security
- Monitor Azure AI services
- Monitor cost
- Create alerts
- View metrics
- Manage diagnostic logging
- Deploy Azure AI services in containers
- Understand containers
- Use Azure AI services containers
- Analyze images
- Provision an Azure AI Vision resource
- Analyze an image
- Generate a smart-cropped thumbnail
- Classify images
- Provision Azure resources for Azure AI Custom Vision
- Understand image classification
- Train an image classifier
- Detect, analyze, and recognize faces
- Identify options for face detection analysis and identification
- Understand considerations for face analysis
- Detect faces with the Azure AI Vision service
- Understand capabilities of the face service
- Compare and match detected faces
- Implement facial recognition
- Read Text in images and documents with the Azure AI Vision Service
- Explore Azure AI Vision options for reading text
- Use the Read API
- Analyze video
- Understand Azure Video Indexer capabilities
- Extract custom insights
- Use Video Analyzer widgets and APIs
- Analyze text with Azure AI Language
- Provision an Azure AI Language resource
- Detect language
- Extract key phrases
- Analyze sentiment
- Extract entities
- Extract linked entities
- Build a question answering solution
- Understand question answering
- Compare question answering to Azure AI Language understanding
- Create a knowledge base
- Implement multi-turn conversation
- Test and publish a knowledge base
- Use a knowledge base
- Improve question answering performance
- Build a conversational language understanding model
- Understand prebuilt capabilities of the Azure AI Language service
- Understand resources for building a conversational language understanding model
- Define intents, utterances, and entities
- Use patterns to differentiate similar utterances
- Use pre-built entity components
- Train, test, publish, and review a conversational language understanding model
- Create a custom text classification solution
- Understand types of classification projects
- Understand how to build text classification projects
- Create a custom named entity extraction solution
- Understand custom named entity recognition
- Label your data
- Train and evaluate your model
- Translate text with Azure AI Translator service
- Provision an Azure AI 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 speech
- Use the Azure AI Speech to Text API
- Use the text to speech API
- Configure audio format and voices
- Use Speech Synthesis Markup Language
- Translate speech with the Azure AI Speech service
- Provision an Azure resource for speech translation
- Translate speech to text
- Synthesize translations
- Create an Azure AI Search solution
- Manage capacity
- Understand search components
- Understand the indexing process
- Search an index
- Apply filtering and sorting
- Enhance the index
- Create a custom skill for Azure AI Search
- Create a custom skill
- Add a custom skill to a skillset
- Create a knowledge store with Azure AI Search
- Define projections
- Define a knowledge store
- Plan an Azure AI Document Intelligence solution
- Understand AI Document Intelligence
- Plan Azure AI Document Intelligence resources
- Choose a model type
- Use prebuilt Azure AI Document Intelligence models
- Understand prebuilt models
- Use the General Document, Read, and Layout models
- Use financial, ID, and tax models
- Extract data from forms with Azure Document Intelligence
- What is Azure Document Intelligence?
- Get started with Azure Document Intelligence
- Train custom models
- Use Azure Document Intelligence models
- Use the Azure Document Intelligence Studio
- Get started with Azure OpenAI Service
- Access Azure OpenAI Service
- Use Azure OpenAI Studio
- Explore types of generative AI models
- Deploy generative AI models
- Use prompts to get completions from models
- Test models in Azure OpenAI Studio's playgrounds
- Build natural language solutions with Azure OpenAI Service
- Integrate Azure OpenAI into your app
- Use Azure OpenAI REST API
- Use Azure OpenAI SDK
- Apply prompt engineering with Azure OpenAI Service
- Understand prompt engineering
- Write more effective prompts
- Provide context to improve accuracy
- Generate code with Azure OpenAI Service
- Construct code from natural language
- Complete code and assist the development process
- Fix bugs and improve your code
- Generate images with Azure OpenAI Service
- What is DALL-E?
- Explore DALL-E in Azure OpenAI Studio
- Use the Azure OpenAI REST API to consume DALL-E models
- Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Understand how to use your own data
- Add your own data source
- Chat with your model using your own data
- Fundamentals of Responsible Generative AI
- Plan a responsible generative AI solution
- Identify potential harms
- Measure potential harms
- Mitigate potential harms
- Operate a responsible generative AI solution