AI-102 Designing and Implementing an Azure AI Solution is intended for software developers who want 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.
After completing this course, students will be able to:
Software engineers interested in building, managing, and deploying AI solutions leveraging Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.They are familiar with C# or Python and have knowledge of using REST-based APIs to build computer vision, language analysis, knowledge extraction, intelligent search, and conversational AI solutions on Azure.
Before attending this course, students must have:
To gain proficiency in C# or Python, you should take the free Take Your First Steps with C# or Take Your First Steps with Python study path before attending the course.
If you are new to artificial intelligence and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.
Module 1: Introduction to AI on Azure
Artificial Intelligence (AI) is increasingly at the core of modern apps and services.
In this module, you will learn about some common AI features that you can leverage in your apps and how these features are implemented in Microsoft Azure.
You will also learn about some considerations for designing and implementing AI solutions responsibly.
Lessons
After completing this module, participants will be able to:
Module 2: Developing AI apps with Cognitive Services
Cognitive Services are the key building blocks for integrating AI capabilities into your apps.
In this module, you will learn how to provision, secure, monitor, and deploy cognitive services.
Lessons
Lab : Getting started with Cognitive Services
Lab : Managing security for Cognitive Services
Lab : Monitoring cognitive services
Lab : Using a Cognitive Services Container
After completing this module, participants will be able to:
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 will learn how to use cognitive services to analyze and translate text.
Lessons
Lab : Analyze textLab : Translate text
After completing this module, participants will be able to:
Module 4: Building applications with speech support
Many modern apps and services accept spoken input and can respond by synthesizing text.
In this module, you will continue to explore natural language processing capabilities by learning how to build speech-enabled applications.
Lessons
Lab: Identifying and synthesizing numbers
Lab : Translate numbers
After completing this module, participants will be able to:
Module 5: Creating solutions for language comprehension
To create an application that can intelligently understand and react to natural language input, you need to define and train a language understanding model.
In this module, you will learn how to use the Language Understanding service to create an app that can identify user intent from natural language input.
Lessons
Lab: Creating a language comprehension app
Laboration : Creating a client application for language understanding
Lab : Using speech and language understanding services
After completing this module, participants will be able to:
Module 6: Building a QnA solution
One of the most common types of interaction between users and AI software agents is that the users submit questions in natural language and the AI agent responds intelligently with an appropriate answer.
In this module, you will explore how the QnA Maker service makes it possible to develop this type of solution.
Lessons
Lab: Creating a QnA solution
After completing this module, participants will be able to:
Module 7: Conversational AI and Azure Bot Service
Bots are the basis for an increasingly common type of AI application where users engage in conversations with AI agents, often in the same way they would with a human agent.
In this module, you will explore the Microsoft Bot Framework and Azure Bot Service, which together form a platform for creating and delivering conversational experiences.
Lessons
Lab: Create a bot with the Bot Framework SDK
Lab: Create a bot with Bot Framework Composer
After completing this module, participants will be able to:
Module 8: Getting started with computer vision
Computer vision is an area of artificial intelligence where software applications interpret visual input from images or video.
In this module, you will begin to explore computer vision by learning how to use cognitive services to analyze images and video.
Lessons
Lab: Analyzing images with computer vision
Lab: Analyzing video with video indexers
After completing this module, participants will be able to:
Module 9: Development of customized vision solutions
There are many scenarios where predefined general computer vision features can be useful, but sometimes you need 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 models for image classification and object detection.
Lessons
Lab: Classifying images with Custom Vision
Lab : Detecting objects in images with Custom Vision
After completing this module, participants will be able to:
Module 10: Detecting, analyzing and recognizing faces
Face detection, analysis and recognition are common scenarios in computer vision.
In this module, you will explore the use of cognitive services to identify human faces.
Lessons
Lab : Detecting, analyzing and recognizing faces
After completing this module, participants will be able to:
Module 11: Reading text in images and documents
Optical Character Recognition (OCR) is another common scenario for computer vision, 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 in images, documents and forms.
Lessons
Lab : Reading text in imagesLab : Extracting data from forms
After completing this module, participants will be able to:
Module 12: Creating a knowledge acquisition solution
Ultimately, many AI scenarios are about intelligently searching for information based on users’ queries.
AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large digital data repositories and enable users to find and analyze those insights.
Lessons
Lab : Creating an Azure Cognitive Search solution
Lab : Create a custom skill for Azure Cognitive Search
Lab : Create a knowledge store with Azure Cognitive Search
After completing this module, participants will be able to:
Course Overview
4 days
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Customized Courses
The course can be adapted from several perspectives:
In interaction with the course leader, we ensure that the course meets your needs.
Send an expression of interest for the training
Send an expression of interest for the training