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

€695.00
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Course Description

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

Public Profile

Software engineers involved in building, managing, and deploying AI solutions using Azure Cognitive Services, Azure Cognitive Search, and the Microsoft Bot Framework. They are well versed in C# or Python, and have knowledge of using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions in Azure.

Goals

  • Describe considerations for developing AI-enabled applications

  • Create, configure, deploy, and secure Azure Cognitive Services

  • Develop applications that analyze text

  • Develop voice-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 machine 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

Elements of this collection

  • Preparation to develop artificial intelligence solutions in Azure (11 Units)
  • Creation and consumption of Cognitive Services (8 Units)
  • Cognitive Services Protection (6 Units)
  • Cognitive Services Supervision (8 Units)
  • Implementation of Cognitive Services in containers (6 Units)
  • Extraction of information from text with the Language service (10 Units)
  • Text translation with the Translator service (8 Units)
  • Creating voice-enabled applications with the Voice service (9 Units)
  • Voice translation with the voice service (7 Units)
  • Creating a Language Understanding application (10 Units)
  • Publishing and using a Language Understanding application (7 Units)
  • Using Language Understanding with Voice (6 Units)
  • Creating a solution to answer questions (12 Units)
  • Creating a bot with the Bot Framework SDK (8 Units)
  • Creating a bot with Bot Framework Composer (8 Units)
  • Image Analysis (7 Units)
  • Analyze videos (7 Units)
  • Image Classification (7 Units)
  • Detection of objects in images (7 Units)
  • Detection, analysis and recognition of faces (10 Units)
  • Reading text in images and documents with the Computer Vision service (7 Units)
  • Extracting data from forms with Form Recognizer (11 Units
  • Creating an Azure Cognitive Search solution (10 Units)
  • Create a custom skill for Azure Cognitive Search (6 Units)
  • Creating a knowledge warehouse with Azure Cognitive Search (6 Units)


Course outline

Module1: Introduction to artificial intelligence in Azure

Artificial intelligence (AI) is increasingly at the heart of modern applications and services. In this module, you will discover some common AI capabilities that you can use in your applications and how those capabilities are implemented in Microsoft Azure. You'll also learn about some considerations for responsibly designing and implementing AI solutions.

Lessons

  • Introduction to artificial intelligence
  • Artificial intelligence in Azure
After completing this module, students will be able to do the following:
  • Describe considerations for creating AI-enabled applications
  • Identify the right Azure services for developing AI applications
Module2: Development of AI applications with Cognitive Services Cognitive

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

Lessons

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

Lab: Introduction to Cognitive Services

Lab: Managing Cognitive Services Security

Lab: Cognitive Services Supervision

Lab: Using Cognitive Services Containers

After completing this module, students will be able to do the following:

  • Provision and consume Cognitive Services in Azure
  • Managing Cognitive Services Security
  • Cognitive Services Monitoring
  • Using Cognitive Services containers

Module3: Introduction to natural language processing

Natural language processing (NLP) is a branch of artificial intelligence that deals with extracting information from written or spoken language. In this module, you will discover how to use Cognitive Services to analyze and translate text.

Lessons

  • Text analysis
  • Text translation

Laboratory: Text Translation

Lab: Text Analysis

After completing this module, students will be able to do the following:

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

Module 4: Creating voice-enabled applications

Many modern applications and services accept spoken input and can respond by synthesizing text. In this module, you will continue your exploration of natural language processing capabilities by learning how to create speech-enabled applications. Lessons

  • Speech recognition and synthesis
  • Speech Translation

Laboratory: Speech Recognition and Synthesis Laboratory: Speech Translation After completing this module, students will be able to do the following:

  • Using the Voice cognitive service to recognize and synthesize speech
  • Using the Voice cognitive service to translate speech

Module 5: Creating Language Understanding Solutions

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

  • Creating a Language Understanding application
  • Publishing and using a Language Understanding app
  • Using Language Understanding with Voice

Lab: Creating a Language Understanding client application Lab: Creating a Language Understanding application Lab: Using Speech and Language Understanding services After completing this module, students will be able to:

  • Creating a Language Understanding application
  • Creating a client application for Language Understanding
  • Language Understanding and Voice Integration

Module 6: Creating a QnA Solution

One of the most common types of interaction between users and AI software agents is for users to submit questions in natural language and for the AI ​​agent to intelligently respond with an appropriate response. In this module, you will explore how the QnA Maker service enables the development of this type of solution. Lessons

  • Creating a QnA KB
  • Publishing and using a QnA KB

Lab: Creating a QnA Solution After completing this module, students will be able to do the following:

  • Use QnA Maker to create a KB
  • Use a QnA KB in an app or bot

Module 7: Conversational AI and Azure Bot Service

Bots are the basis of an increasingly common type of AI application in which users interact in conversations with AI agents, sometimes just as they would with a human agent. In this module, you will explore the Microsoft Bot Framework and Azure Bot Service, which together provide a platform for creating and delivering conversational experiences. Lessons

  • Bot Basics
  • Implementation of a chatbot

Lab: Create a bot with the Bot Framework SDK Lab: 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

Module 8: Introduction to 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 will begin your exploration of computer vision by discovering how to use cognitive services to analyze images and videos. Lessons

  • Image analysis
  • Video analysis

Laboratory: Video Analysis Laboratory: Image Analysis with Computer Vision After completing this module, students will be able to do the following:

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

Module 9: Custom Vision Solution Development

Although there are many scenarios where general predefined computer vision capabilities can be useful, sometimes it is necessary to train a custom model with your own visual data. In this module, you will explore the Custom Vision service and how to use it to create custom image classification and object detection models. Lessons

  • Image classification
  • Object detection

Lab: Image Classification with Custom Vision Lab: Object Detection in Images with Custom Vision After completing this module, students will be able to do the following:

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

Module 10: Face detection, analysis and recognition

Face detection, analysis, and recognition are common computer vision scenarios. In this module, you will explore the Cognitive Services user to identify human faces. Lessons

  • Face detection with Computer Vision service
  • Use of the Face service

Lab: Face Detection, Analysis, and Recognition After completing this module, students will be able to do the following:

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

Module 11: Reading text from images and documents

Optical character recognition (OCR) is another common Computer Vision scenario, in which the software extracts text from images or documents. In this module, you will explore cognitive services that can be used to detect and read text in images, documents, and forms. Lessons

  • Reading text with the Computer Vision service
  • Extracting information from forms with the Form Recognizer service

Lab: Reading Text in Images Lab: Extracting Data from Forms After completing this module, students will be able to do the following:

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

Module 12: Creating a Knowledge Mining Solution

At their core, many AI scenarios involve the intelligent search for information based on user queries. AI-powered knowledge mining is an increasingly important way to create intelligent search solutions that use AI to extract information from large repositories of digital data, as well as enable users to search and analyze that information. Lessons

  • Implementation of a smart search solution
  • Develop custom skills for an enrichment pipeline
  • Creating a knowledge warehouse

Lab: Create a custom skill for Azure Cognitive Search Lab: Create an Azure Cognitive Search solution Lab: 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
  • Deploy a custom skill to an Azure Cognitive Search enrichment pipeline
  • Use Azure Cognitive Search to create a knowledge store

Previous requirements

Before attending this course, students 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

To gain C# or Python skills, complete the free Getting Started with C# or Python for Beginners learning paths before attending the course. If you are new to AI and want to get an overview of AI capabilities in Azure, consider completing the Azure AI Fundamentals certification before taking this one.

Language

  • Course: English / Spanish
  • Labs: English

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