Live Microsoft AI-900 & AI-102 Training Course
Microsoft AI Fundamentals to Designing AI Solutions (AI-900 & AI-102)
| Course Number: |
#CED-1802 |
| Course Length: |
5 days |
| Number of Exams: |
2 |
| Certifications: |
Microsoft Certified: Azure AI Fundamentals
Microsoft Certified: Azure AI Engineer Associate |
Grants (discounts) are available for multiple students for the same or different courses. |
Instructor-Led
Certified Instructor
Includes all course materials
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Remote Classroom Training
Our Remote Classroom Training is a live class with students observing the instructor and listening through your computer speakers.
You will see the instructor's computer, slides, notes, etc., just like in the classroom. You will be following along, doing work, labs, and individual assignments.
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"Instructor is very knowledgeable and very helpful, doing well to keep the class on task. The instructor does everything to help us prepare and be ready for exams. We had a great group. We worked hard and had a good time in class. Our instructor was awesome!" -J McDonald
Microsoft Course AI-900 introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions.
Microsoft Course AI-102 is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI.
Microsoft Course AI-900: Microsoft Azure AI Fundamentals
This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them.
Who Should Attend?
The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don't need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.
Course Prerequisites
Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically:
- Experience using computers and the internet.
- Interest in use cases for AI applications and machine learning models.
- A willingness to learn through hands-on experience
Agenda
1 - Fundamental AI Concepts
- Understand machine learning
- Understand computer vision
- Understand natural language processing
- Understand document intelligence and knowledge mining
- Understand generative AI
- Challenges and risks with AI
- Understand Responsible AI
2 - Fundamentals of machine learning
- What is machine learning?
- Types of machine learning
- Regression
- Binary classification
- Multiclass classification
- Clustering
- Deep learning
- Azure Machine Learning
3 - Fundamentals of Azure AI services
- AI services on the Azure platform
- Create Azure AI service resources
- Use Azure AI services
- Understand authentication for Azure AI services
4 - Fundamentals of Computer Vision
- Images and image processing
- Machine learning for computer vision
- Azure AI Vision
5 - Fundamentals of Facial Recognition
- Understand Face analysis
- Get started with Face analysis on Azure
6 - Fundamentals of optical character recognition
- Get started with Azure AI Vision
- Get started with Vision Studio on Azure
7 - Fundamentals of Text Analysis with the Language Service
- Understand Text Analytics
- Get started with text analysis
8 - Fundamentals of question answering with the Language Service
- Understand question answering
- Get started with the Language service and Azure Bot Service
9 - Fundamentals of conversational language understanding
- Describe conversational language understanding
- Get started with conversational language understanding in Azure
10 - Fundamentals of Azure AI Speech
- Understand speech recognition and synthesis
- Get started with speech on Azure
11 - Fundamentals of Azure AI Document Intelligence
- Explore capabilities of document intelligence
- Get started with receipt analysis on Azure
12 - Fundamentals of Knowledge Mining and Azure AI Search
- What is Azure AI Search?
- Identify elements of a search solution
- Use a skillset to define an enrichment pipeline
- Understand indexes
- Use an indexer to build an index
- Persist enriched data in a knowledge store
- Create an index in the Azure portal
- Query data in an Azure AI Search index
13 - Fundamentals of Generative AI
- What is generative AI?
- What are language models?
- Using language models
- What are copilots?
- Microsoft Copilot
- Considerations for Copilot prompts
- Extending and developing copilots
14 - Fundamentals of Azure OpenAI Service
- What is generative AI
- Describe Azure OpenAI
- How to use Azure OpenAI
- Understand OpenAI's natural language capabilities
- Understand OpenAI code generation capabilities
- Understand OpenAI's image generation capabilities
- Describe Azure OpenAI's access and responsible AI policies
15 - 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
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Microsoft Course AI-102: Designing and Implementing an Azure AI Solution
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.
Who Should Attend?
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.
Course Prerequisites
Before attending this course, students must 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
- Recommended course prerequisites
- AI-900T00: Microsoft Azure AI Fundamentals course
Agenda
1 - 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 OpenAI Service
- Understand capabilities of Azure AI Search
2 - Create and consume Azure AI services
- Provision an Azure AI services resource
- Identify endpoints and keys
- Use a REST API
- Use an SDK
3 - Secure Azure AI services
- Consider authentication
- Implement network security
4 - Monitor Azure AI services
- Monitor cost
- Create alerts
- View metrics
- Manage diagnostic logging
5 - Deploy Azure AI services in containers
- Understand containers
- Use Azure AI services containers
6 - Use AI responsibly with Azure AI Content Safety
- What is Content Safety
- How does Azure AI Content Safety work?
- When to use Azure AI Content Safety
7 - Analyze images
- Provision an Azure AI Vision resource
- Analyze an image
- Generate a smart-cropped thumbnail and remove background
8 - Image classification with custom Azure AI Vision models
- Understand custom model types
- Create a custom project
- Label and train a custom model
9 - Classify images
- Provision Azure resources for Azure AI Custom Vision
- Understand image classification
- Train an image classifier
10 - Detect objects in images
- Understand object detection
- Train an object detector
- Consider options for labeling images
11 - 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
12 - Read Text in images and documents with the Azure AI Vision Service
- Explore Azure AI Vision options for reading text
- Use the Read API
13 - Analyze video
- Understand Azure Video Indexer capabilities
- Extract custom insights
- Use Video Analyzer widgets and APIs
14 - Analyze text with Azure AI Language
- Provision an Azure AI Language resource
- Detect language
- Extract key phrases
- Analyze sentiment
- Extract entities
- Extract linked entities
15 - Create question answering solutions with Azure AI Language
- 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
16 - 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
17 - Create a custom text classification solution
- Understand types of classification projects
- Understand how to build text classification projects
18 - Custom named entity recognition
- Understand custom named entity recognition
- Label your data
- Train and evaluate your model
19 - Translate text with Azure AI Translator service
- Provision an Azure AI Translator resource
- Specify translation options
- Define custom translations
20 - 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
21 - Translate speech with the Azure AI Speech service
- Provision an Azure resource for speech translation
- Translate speech to text
- Synthesize translations
22 - 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
23 - Create a custom skill for Azure AI Search
- Define the custom skill schema
- Add a custom skill
- Custom text classification skill
- Machine learning custom skill
24 - Create a knowledge store with Azure AI Search
- Define projections
- Define a knowledge store
25 - Implement advanced search features in Azure AI Search
- Improve the ranking of a document with term boosting
- Improve the relevance of results by adding scoring profiles
- Improve an index with analyzers and tokenized terms
- Enhance an index to include multiple languages
- Improve search experience by ordering results by distance from a given reference point
26 - Search data outside the Azure platform in Azure AI Search using Azure Data Factory
- Index data from external data sources using Azure Data Factory
- Index any data using the Azure AI Search push API
27 - Maintain an Azure AI Search solution
- Manage security of an Azure AI Search solution
- Optimize performance of an Azure AI Search solution
- Manage costs of an Azure AI Search solution
- Improve reliability of an Azure AI Search solution
- Monitor an Azure AI Search solution
- Debug search issues using the Azure portal
28 - Perform search reranking with semantic ranking in Azure AI Search
- What is semantic ranking?
- Set up semantic ranking
29 - Perform vector search and retrieval in Azure AI Search
- What is vector search?
- Prepare your search
- Understand embedding
30 - Plan an Azure AI Document Intelligence solution
- Understand AI Document Intelligence
- Plan Azure AI Document Intelligence resources
- Choose a model type
31 - Use prebuilt Document intelligence models
- Understand prebuilt models
- Use the General Document, Read, and Layout models
- Use financial, ID, and tax models
32 - 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
33 - Create a composed Document intelligence model
- Understand composed models
- Assemble composed models
34 - Get started with Azure OpenAI Service
- Access Azure OpenAI Service
- Use Azure AI Studio
- Explore types of generative AI models
- Deploy generative AI models
- Use prompts to get completions from models
- Test models in Azure AI Studio's playground
35 - Build natural language solutions with Azure OpenAI Service
- Integrate Azure OpenAI into your app
- Use Azure OpenAI REST API
- Use Azure OpenAI SDK
36 - Apply prompt engineering with Azure OpenAI Service
- Understand prompt engineering
- Write more effective prompts
- Provide context to improve accuracy
37 - Generate code with Azure OpenAI Service
- Construct code from natural language
- Complete code and assist the development process
- Fix bugs and improve your code
38 - Generate images with Azure OpenAI Service
- What is DALL-E?
- Explore DALL-E in Azure AI Studio
- Use the Azure OpenAI REST API to consume DALL-E models
39 - Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Understand Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Add your own data source
- Chat with your model using your own data
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