Microsoft Azure
Designing and Implementing Microsoft Azure AI Solutions
About the Course: Designing and Implementing Microsoft Azure AI Solutions
This course is designed to provide a comprehensive understanding about designing and implementing Microsoft Azure AI solutions. The course starts by identifying the benefits of AI and how it can enhance business applications. Participants will explore the different AI tools, including cognitive services, machine learning, knowledge mining, and computer vision, and learn how to use them to develop intelligent applications.
The course will also cover important concepts such as data preparation, model training and evaluation, and deployment of intelligent solutions. The course will use real-life examples to help participants understand the practical application of AI solutions in different industries.
Throughout the course, participants will also learn about ethical considerations when developing AI solutions, as well as techniques for ensuring security and privacy of data. By the end of the course, learners will be equipped with the skills necessary to design and deploy AI solutions on the Azure platform.
Audience Profile
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI.
Your responsibilities include participating in all phases of AI solutions development, including:
- Plan and manage an Azure AI solution (15–20%)
- Implement decision support solutions (10–15%)
- Implement computer vision solutions (15–20%)
- Implement natural language processing solutions (30–35%)
- Implement knowledge mining and document intelligence solutions (10–15%)
- Implement generative AI solutions (10–15%)
Modules
In this module, you’ll learn about common uses of artificial intelligence (AI), and the different types of the workload associated with AI. You’ll then explore considerations and principles for responsible AI development.
- Artificial Intelligence in Azure
- Responsible AI
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
Computer vision is an 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
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 analyse text, recognise and synthesize speech, translate between languages, and interpret commands.
- Describe features of Natural Language Processing (NLP) workloads on Azure
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.
This course aims to equip participants with the necessary skills and knowledge to successfully undertake the Microsoft AI-900: Microsoft Azure AI Fundamentals examination.
