AI-102T00: Designing and Implementing a Microsoft Azure AI Solution

Characteristics

AI-102 Designing and Implementing a Microsoft Azure AI Solution is intended for software developers who want to build AI-powered applications using Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. C# or Python programming language will be used during the course.

Requirements

Before attending this course, participants must have:

• Knowledge of Microsoft Azure and ability to navigate the Azure portal
• Knowledge of C# or Python
• Knowledge of JSON and REST programming semantics

Lessons

• Introduction to Artificial Intelligence
• Artificial Intelligence on Azure

Artificial Intelligence (AI) is increasingly at the core of modern applications and services. In this module, you will learn about typical AI capabilities you can leverage in your applications and how they are implemented in Microsoft Azure. You will also learn about considerations for designing and implementing AI solutions responsibly.

After completing this module, students will be able to:

• Describe considerations for creating AI-enabled applications
• Identify Azure services for creating AI applications

Lessons

• Introduction to Cognitive Services
• Using Cognitive Services for enterprise applications

Cognitive Services are the essential building blocks for integrating AI capabilities into applications. In this module, you will learn how to provision, secure, monitor, and deploy cognitive services.

Labs:

• Get Started with Cognitive Services
• Manage Cognitive Services Security
• Monitor Cognitive Services
• Use a Cognitive Services Container

After completing this module, students will be able to:

• Provision and consume cognitive services in Azure
• Manage cognitive services security
• Monitor cognitive services
• Use a cognitive services container

Lessons

• Analyzing Text
• Translating Text

Natural Language Processing (NLP) is a branch of artificial intelligence concerned with extracting insights from written or spoken language. In this module, you will learn how to use cognitive services to analyze and translate text.

Labs:

• Translate Text
• Analyze Text

After completing this module, students will be able to:

• Use the Text Analytics cognitive service to analyze text
• Use the Translator cognitive service to translate text

Lessons

• Speech Recognition and Synthesis
• Translating Speech

Many modern applications and services accept speech input and can respond by synthesizing text. In this module, continuing your exploration of natural language processing capabilities, you will learn how to build speech-enabled applications.

Labs:

• Speech Recognition and Synthesis
• Translate Speech

After completing this module, students will be able to:

• Use the Speech cognitive service to recognize and synthesize speech
• Use the Speech cognitive service to translate speech

Lessons

• Creating a Language Understanding App
• Publishing and Using a Language Understanding App
• Using Language Understanding with Speech

To build an application that can intelligently understand and respond to natural language input, you must define and train a language understanding model. In this module, you will learn how to use the Language Understanding service to build an application that can identify user intent from natural language input.

Labs:

• Create a Language Understanding Client Application
• Create a Language Understanding App
• Use Speech and Language Understanding Services

After completing this module, students will be able to:

• Create a Language Understanding app
• Create a client application for Language Understanding
• Integrate Language Understanding and speech

Lessons

• Creating a QnA Knowledge Base
• Publishing and Using a QnA Knowledge Base

One of the most common types of interaction between users and AI software agents is asking natural language questions, to which the AI agent intelligently responds with the appropriate answer. In this module, you will learn how the QnA Maker service enables you to build this type of solution.

Labs:

• Create a QnA Solution

After completing this module, students will be able to:

• Use the QnA Maker service to create a knowledge base
• Use a QnA knowledge base in an application or bot

Lessons

• Bot Basics
• Implementing a Conversational Bot

Bots form the foundation of an increasingly common type of AI application where users converse with AI agents, often just as they would with a human. In this module, you will explore the Microsoft Bot Framework and Azure Bot Service, which together provide a platform for building and delivering conversational experiences.

Labs:

• Create a Bot with the Bot Framework SDK
• Create a Bot with Bot Framework Composer

After completing this module, students will be able to:

• Use the Bot Framework SDK to create a bot
• Use Bot Framework Composer to create a bot

Lessons

• Analyzing Images
• Analyzing Video

Computer vision is an area of artificial intelligence in which applications interpret visual input from images or video. In this module, you will start exploring computer vision by learning how to use cognitive services to analyze images and video.

Labs:

• Analyze Video
• Analyze Images with Computer Vision

After completing this module, students will be able to:

• Use the Computer Vision service to analyze images
• Use Video Analyzer to analyze video

Lessons

• Image Classification
• Object Detection

While predefined general computer vision capabilities are useful in many scenarios, sometimes you need to train a custom model on your own visual data. In this module, you will explore the Custom Vision service and learn how to use it to build custom image classification and object detection models.

Labs:

• Classify Images with Custom Vision
• Detect Objects in Images with Custom Vision

After completing this module, students will be able to:

• Use the Custom Vision service to implement image classification
• Use the Custom Vision service to implement object detection

Lessons

• Face Detection with Computer Vision
• Using the Face Service

Detecting, analyzing, and recognizing faces are common computer vision scenarios. In this module, you will learn how to use cognitive services to identify human faces.

Labs:

• Detect, Analyze, and Recognize Faces

After completing this module, students will be able to:

• Detect faces with Computer Vision
• Detect, analyze, and recognize faces with the Face service

Lessons

• Reading Text with Computer Vision
• Extracting Information from Forms with Form Recognizer

Optical Character Recognition (OCR) is another common computer vision scenario where software extracts text from images or documents. In this module, you will explore cognitive services that can be used to detect and read text from images, documents, and forms.

Labs:

• Read Text from Images
• Extract Data from Forms

After completing this module, students will be able to:

• Use the Computer Vision service to read text from images and documents
• Use the Form Recognizer service to extract data from digital forms

Lessons

• Implementing an Intelligent Search Solution
• Creating Custom Skills for an Enrichment Pipeline
• Creating a Knowledge Store

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 leverage AI to extract insights from large digital data repositories and enable users to find and analyze those insights.

Labs:

• Create a Custom Skill for Azure Cognitive Search
• Create an Azure Cognitive Search Solution
• Create a Knowledge Store with Azure Cognitive Search

After completing this module, students will be able to:

• Create an intelligent search solution with Azure Cognitive Search
• Implement a custom skill in an Azure Cognitive Search enrichment pipeline
• Use Azure Cognitive Search to create a knowledge store