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MS-AI900T00: Microsoft Azure AI Fundamentals
Course Code: MS-AI900T00
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. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.
This course is designed for is designed for individuals who want to gain a foundational understanding of AI and its applications using Microsoft Azure. The primary audience for this course includes:
- Beginners in AI: Those who are new to artificial intelligence and want to learn the basics.
- IT Professionals: Individuals working in IT who want to understand how AI can be integrated into their workflows.
- Business Decision Makers: Managers and executives who need to understand AI concepts to make informed decisions about AI solutions.
- Students and Educators: Those in academic settings who are interested in learning or teaching the fundamentals of AI.
- Anyone interested in AI: Individuals from both technical and non-technical backgrounds who want to explore AI capabilities and services offered by Microsoft Azure.
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 exploration.
After completing this course, students will be able to:
- Describe Artificial Intelligence workloads and considerations
- Describe fundamental principles of machine learning on Azure
- Describe features of computer vision workloads on Azure
- Describe features of Natural Language Processing (NLP) workloads on Azure
- Describe features of conversational AI workloads on Azure
This course will prepare delegates to write the Microsoft AI-900: Microsoft Azure AI Fundamentals exam.
Modules
With AI, we can build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.
Lessons
- Introduction to AI.
- 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.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Learn about the kinds of solutions AI can make possible and considerations for responsible AI practices.
Machine learning is the basis for most modern artificial intelligence solutions. A familiarity with the core concepts on which machine learning is based is an important foundation for understanding AI.
Lessons
- Introduction.
- What is machine learning?
- Types of machine learning.
- Regression.
- Binary classification.
- Multiclass classification.
- Clustering.
- Deep learning.
- Azure Machine Learning.
- Exercise - Explore Automated Machine Learning in Azure Machine Learning.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Describe core concepts of machine learning.
- Identify different types of machine learning.
- Describe considerations for training and evaluating machine learning models.
- Describe core concepts of deep learning.
- Use automated machine learning in Azure Machine Learning service.
In this module, you learn the fundamentals of how Azure AI services can be used to build applications.
Lessons
- Introduction.
- AI services on the Azure platform.
- Create Azure AI service resources.
- Use Azure AI services.
- Understand authentication for Azure AI services.
- Exercise - Explore Azure AI Services.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- Learn how to train and publish a regression model with Azure Machine Learning designer.
- Understand applications Azure AI services can be used to build.
- Understand how to access Azure AI services in the Azure portal.
- Understand how to use Azure AI services keys and endpoint for authentication.
- Create and use an Azure AI services resource in a Content Safety Studio setting.
Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.
Lessons
- Introduction to Machine Learning
- Azure Machine Learning
After completing this module you will be able to
- Describe fundamental principles of machine learning on Azure
After completing this module, you will be able to:
- Learn how to use the Azure AI Vision service to analyse images.
Face detection, analysis, and recognition are important capabilities for artificial intelligence (AI) solutions. Azure AI Face service in Azure makes it easy integrate these capabilities into your applications.
Lessons
- Introduction.
- Understand Face analysis.
- Get started with Face analysis on Azure.
- Exercise - Detect faces in Vision Studio.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Learn how to use Azure AI Face service to detect and analyse faces in images.
Optical character recognition (OCR) enables artificial intelligence (AI) systems to read text in images, enabling applications to extract information from photographs, scanned documents, and other sources of digitized text.
Lessons
Introduction.
Get started with Vision Studio on Azure.
Exercise - Read the text in Vision Studio.
Knowledge check.
Summary.
After completing this module, you will be able to:
- Learn how to read text in images with Azure AI Vision.
Explore Azure AI Language's natural language processing (NLP) features, which include sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Lessons
- Introduction.
- Understand Text Analytics.
- Get started with text analysis.
- Exercise - Analyse text with Language Studio.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- Learn how to use Azure AI Language for text analysis.
Create a custom question-answering knowledge base with Azure AI Language and create a bot with Azure AI Bot Service that answers user questions.
Lessons
- Introduction.
- Understand question answering.
- Get started with the Language service and Azure Bot Service.
- Exercise - Use question answering with Language Studio.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- Understand how to use Azure AI Language to create a custom question-answering project.
In this module, we introduce you to conversational language understanding and show how to create applications that understand language with Azure AI Language.
Lessons
- Introduction.
- Describe conversational language understanding.
- Get started with conversational language understanding in Azure.
- Exercise - Use Conversational Language Understanding with Language Studio.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Learn what conversational language understanding is.
- Learn about key features, such as intents and utterances.
- Build and publish a natural-language machine-learning model
In this module, we introduce you to conversational language understanding, and show how to create applications that understand language with Azure AI Language.
Lessons
- Introduction.
- Understand speech recognition and synthesis.
- Get started with speech on Azure.
- Exercise - Explore Speech Studio.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Learn about speech recognition and synthesis.
- Learn how to use Azure AI Speech.
Document processing is a common task in many business scenarios. Organizations can use Azure AI Document Intelligence to automate data extraction across document types, such as receipts, invoices, and more.
Lessons
- Introduction.
- Explore capabilities of document intelligence.
- Get started with receipt analysis on Azure.
- Exercise - Extract from data in Document Intelligence Studio.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Learn how to use the prebuilt receipt processing capabilities of Azure AI Document Intelligence.
Use Azure AI Search to make your data searchable.
Lessons
- Introduction.
- 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.
- Exercise - Explore an Azure AI Search index (UI).
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Explore Azure AI Search.
- Create an Azure AI Search index.
- Import data to the index.
- Query the Azure AI Search index.
In this module you'll explore the way in which large language models (LLMs) enable AI applications and services to generate original content based on natural language input. You’ll also learn how generative AI enables the creation of AI-powered copilots that can assist humans in creative tasks.
Lessons
- Introduction.
- What is generative AI?
- Large language models.
- What is Azure OpenAI?
- What are copilots?
- Improve generative AI responses with prompt engineering.
- Exercise - Explore generative AI with Bing Copilot.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Understand generative AI's place in the development of artificial intelligence.
- Understand large language models and their role in intelligent applications.
- Describe how Azure OpenAI supports intelligent application creation.
- Describe examples of copilots and good prompts.
Get to know the connection between artificial intelligence (AI), Responsible AI, and text, code, and image generation. Understand how you can use Azure OpenAI to build solutions against AI models within Azure.
Lessons
- Introduction.
- 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.
- Exercise - Explore Azure OpenAI Service.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Describe Azure OpenAI workloads and access the Azure OpenAI Service.
- Understand generative AI models.
- Understand Azure OpenAI's language, code, and image capabilities.
- Understand Azure OpenAI's responsible AI practices and limited access policies.
Generative AI enables amazing creative solutions but must be implemented responsibly to minimize the risk of harmful content generation.
Lessons
- Introduction.
- Plan a responsible generative AI solution.
- Identify potential harms.
- Measure potential harms.
- Mitigate potential harms.
- Operate a responsible generative AI solution.
- Exercise - Explore content filters in Azure OpenAI.
- Knowledge check.
- Summary.
After completing this module, you will 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.