AI-900: Microsoft Azure AI Fundamentals

About Course

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.

Course Objectives

  • 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

Course Outline

Module 1:  Introduction to AI

In this module, you’ll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You’ll then explore considerations and principles for responsible AI development.

  • Artificial Intelligence in Azure
  • Responsible AI

After completing this module, you will be able to:

  • Describe Artificial Intelligence workloads and considerations

Module 2:  Machine Learning

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.

  • Introduction to Machine Learning
  • Azure Machine Learning

After completing this module, you will be able to:

  • Describe fundamental principles of machine learning on Azure

Module 3:  Computer Vision

Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you’ll explore multiple computer vision techniques and services.

  • Computer Vision Concepts
  • Computer Vision in Azure

After completing this module, you will be able to:

  • Describe features of computer vision workloads on Azure

Module 4:  Natural Language Processing

This module describes scenarios for AI solutions that can process written and spoken language. You’ll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands.

After completing this module, you will be able to:

  • Describe features of Natural Language Processing (NLP) workloads on Azure

Module 5:  Conversational AI

Conversational AI enables users to engage in a dialog with an AI agent, or *bot*, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.

  • Conversational AI Concepts
  • Conversational AI in Azure

After completing this module, you will be able to:

  • Describe features of conversational AI workloads on Azure

World Food Programme (WFP)

Our work with the World Food Programme (WFP) focused on enabling the effective adoption of digital field technologies and essential digital literacy capabilities. Participants utilized mobile-based data collection platforms within operational contexts, enhancing accuracy, consistency, and confidence in digital data handling. The engagement strengthened WFP’s ability to rely on digital tools to support field operations and humanitarian programs.

Raya

For Raya, we delivered technology enablement focused on automation-driven operations and scalable application development. Participants gained hands-on experience with automation technologies and modern front-end development frameworks, supporting more efficient processes and the delivery of flexible, high-performance digital solutions aligned with business growth objectives.

EgyptAir

Our engagement with EgyptAir focused on enabling the effective use of application development technologies alongside the adoption of cybersecurity and secure computing practices within operational environments. Participants worked with Microsoft-based development platforms and programming technologies while gaining practical exposure to secure application usage, access control mechanisms, and threat-aware system interaction. This integrated technology enablement supported more secure digital operations, improved system reliability, and reinforced cyber resilience across aviation technology environments.

Banque Misr

We collaborated with Banque Misr to enable integrated enterprise technology capabilities across multiple domains. The engagement supported effective utilization of IT infrastructure environments, data analytics platforms, and professional capability development frameworks, allowing teams to operate confidently within complex enterprise systems. Our delivery approach focused on practical technology adoption, operational alignment, and building sustainable competencies that support reliable banking services and informed, data-driven decision-making.

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