AI-102 Designing and Implementing a Microsoft Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.
In this course you will learn about :
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. 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 conversational AI solutions on Azure.
Before attending this course, students must have:
AI-102: Designing and Implementing a Microsoft Azure AI Solution
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.
After completing this module, students will be able to:
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.
Lab : Get Started with Cognitive Services
Lab : Manage Cognitive Services Security
Lab : Monitor Cognitive Services
Lab : Use a Cognitive Services Container
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.
Lab : Analyze Text
Lab : 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.
Lab : Recognize and Synthesize Speech
Lab : Translate Speech
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.
Lab : Create a Language Understanding App
Lab : Create a Language Understanding Client Application
Lab : Use the Speech and Language Understanding Services
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.
Lab : Create a QnA 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.
Lab : Create a Bot with the Bot Framework SDK
Lab : Create a Bot with Bot Framework Composer
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.
Lab : Analyze Images with Computer Vision
Lab : Analyze Video with Video Indexer
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.
Lab : Classify Images with Custom Vision
Lab : Detect Objects in Images with Custom Vision
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.
Lab : Detect, Analyze, and Recognize 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.
Lab : Read Text in Images
Lab : Extract Data from 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.
Lab : Create an Azure Cognitive Search solution
Lab : Create a Custom Skill for Azure Cognitive Search
Lab : Create a Knowledge Store with Azure Cognitive Search