ICT 912 - Designing and Implementing a Microsoft Azure AI Solution
In this course, you will learn how to develop AI solutions on Azure, using Azure Cognitive Services, Azure Bot Service and Azure Cognitive Search. You will build on your existing software development skills, including experience of programming with Python, and familiarity with REST and JSON.
This course covers the objectives for Microsoft Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution. This exam is required to fulfil the requirements of the Microsoft Certified: Azure AI Engineer Associate professional certification. Discounted exam vouchers for AI-102 will be available for purchase for all learners (optional).
Course DetailsLearning Outcomes
By completion of this course, successful students will be able to:
- Describe considerations for AI-enabled application development
- Create, configure, deploy, and secure Azure Cognitive Services
- Develop applications that analyze text
- Develop speech-enabled applications
- Create applications with natural language understanding capabilities
- Create QnA applications
- Create conversational solutions with bots
- Use computer vision services to analyze images and videos
- Create custom computer vision models
- Develop applications that detect, analyze, and recognize faces
- Develop applications that read and process text in images and documents
- Create intelligent search solutions for knowledge mining
Module 1: Introduction to AI on Azure
Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you'll learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You'll also learn about some considerations for designing and implementing AI solutions responsibly.
Module 2: Developing AI Apps with Cognitive Services
Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you'll learn how to provision, secure, monitor, and deploy cognitive services.
Module 3: Getting Started with Natural Language Processing
Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you'll learn how to use cognitive services to analyze and translate text.
Module 4: Building Speech-Enabled Applications
Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you'll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications.
Module 5: Creating Language Understanding Solutions
To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you'll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input.
Module 6: Building a QnA Solution
One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you'll explore how the QnA Maker service enables the development of this kind of solution.
Module 7: Conversational AI and the Azure Bot Service
Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you'll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences.
Module 8: Getting Started with Computer Vision
Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you'll start your exploration of computer vision by learning how to use cognitive services to analyze images and video.
Module 9: Developing Custom Vision Solutions
While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you'll explore the Custom Vision service, and how to use it to create custom image classification and object detection models.
Module 10: Detecting, Analyzing, and Recognizing Faces
Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you'll explore the user of cognitive services to identify human faces.
Module 11: Reading Text in Images and Documents
Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you'll explore cognitive services that can be used to detect and read text in images, documents, and forms.
Module 12: Creating a Knowledge Mining Solution
Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights.
Who is this course for?
Individuals who are keen to learn about building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
This course is designed for those who wish to pursue future careers like:
- AI Software Developer
- AI Engineer
- AI Solution Specialist / Consultant
This course includes hands-on activities to reinforce the concepts taught and provide a practical learning experience.
To complete the labs, you will require:
- A development environment that includes Visual Studio Code, Python, the Bot Framework Composer, and the Bot Framework Emulator
- The lab files for this course, which are published online at https://microsoftlearning.github.io/AI-102-AIEngineer/
- Microsoft Azure Pass for Lab (will be provided at no additional cost from Section 3 onwards)
Before attending this course, students must have:
- Knowledge of Microsoft Azure and ability to navigate the Azure portal
- Knowledge of Python
- Familiarity with JSON and REST programming semantics
To gain Python skills, complete the free Take your first steps with Python learning path before attending the course.
If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the ICT 901 Azure AI Fundamentals before taking this one.