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

Overview

AI-102 Course, Training and Certification: Overview

Master the art of designing and implementing an Azure AI solution with SpireTec and reach new heights of success in your professional sphere. Our online training helps you learn the skills to plan and manage an Azure Cognitive Services solution. Our experienced instructor tells you how to implement Computer Vision solutions, natural language processing solutions, and knowledge mining solutions, and eventually help end users to make the most of the conversational AI solutions. The knowledge helps you clear AI 102 certification training successfully.

With organizations focused at taping in on the secure, and responsible AI capabilities, the demand for candidates with AI 102 certification has increased. The certification validates your skills regarding designing and implementing an Azure AI solution. Currently, there is a big gap between demand and supply, means the job positions and the talents. The shortage of talents in this field, puts AI 102 certification holder at a demanding position. They can expect a higher remuneration.

Microsoft AI 102 Certification: Breakup of the Exam Topics

  1. Plan and manage an Azure Cognitive Services solution
  2. Implement Computer Vision solutions
  3. Implement natural language processing solutions
  4. Implement knowledge mining solutions
  5. Implement conversational AI solutions

Who Should Take AI 102 Training?

Azure Solution Architect

Azure Solution architects are responsible for designing simple to complex and small to large enterprise solutions. Designing and implementing an Azure AI solution is an integral part of their core job. They move the AI project through analysis, design and planning phases. They have thorough knowledge of Application Lifecycle Management, Gap Analysis, Estimation and Project Management. They often have to handle client communication part.

Software Developers

Product based companies prefer candidates with AI 102 certification. Designing and implementing an Azure AI solution involves working with Azure Files Systems, Azure Event Grid, Azure App Service, and Azure Security.

Information Security Professionals

The job profiles include implementing and enhancing the security event information management (SIEM), configuring, maintaining, fine-tuning and managing the web application firewalls. The certification on designing and implementing an Azure AI solution helps candidates to scale new career heights and get a higher compensation than those who do not have it.

Prerequisites for AI 102 Certification

Candidates for this exam should be proficient in C#, Python, or JavaScript and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure. They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Full Description

Course outline

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.

Lessons

  • Introduction to Artificial Intelligence
  • Artificial Intelligence in Azure

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.

Lessons

  • Getting Started with Cognitive Services
  • Using Cognitive Services for Enterprise Applications

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.

Lessons

  • Analyzing Text
  • Translating 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.

Lessons

  • Speech Recognition and Synthesis
  • Speech Translation

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.

Lessons

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

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.

Lessons

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

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.

Lessons

  • Bot Basics
  • Implementing a Conversational Bot

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.

Lessons

  • Analyzing Images
  • Analyzing Videos

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.

Lessons

  • Image Classification
  • Object Detection

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.

Lessons

  • Detecting Faces with the Computer Vision Service
  • Using the Face Service

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.

Lessons

  • Reading text with the Computer Vision Service
  • Extracting Information from Forms with the Form Recognizer service

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.

Lessons

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

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

Fees & Schedule

Delivery Mode Course Duration Fees
Live Virtual Training 4 Days Ask for Quote
Onsite Classroom Training 4 Days Ask for Quote
Customized Training 4 Days Ask for Quote

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