Azure AI Engineers use Cognitive Services, Machine Learning, and Knowledge Mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, bots, and agents.
Exam AI-100: Designing and Implementing an Azure AI Solution
This exam measures your ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.
The following course prepare the exam AI-100:
Course AI-100: Designing and Implementing an Azure AI Solution
An Azure AI engineer works with Data Engineers and Data Scientists to analyze requirements for AI cloud-based and hybrid AI solutions and implements solutions. They are aware of the various components that make up the Microsoft Azure AI portfolio and related open source frameworks and technologies. The engineer leverages their knowledge to recommend appropriate tools and technologies for a given solution. The engineer is aware of the available data storage options and uses their understanding of cost models, capacity, and best practices to architect and implement AI solutions. Designing and Implementing an Azure AI Solution course AI-100
This course teaches the concepts of Azure AI engineering by presenting, and developing, a scenario that creates a customer support Bot that utilizes various tools and services in the Azure AI landscape like language understanding, QnA Maker, and various Azure Cognitive Services to implement language detection, text analytics, and computer vision.
Course Outline
Module 1: Introducing Azure Cognitive Services
The student will learn about the available Cognitive Services on Microsoft Azure and their role in architecting AI solutions.
Lessons
Overview of Azure Cognitive Services
Creating a Cognitive Service on the Azure Portal
Access and Test a Cognitive Service
Module 2: Creating Bots
The student will learn about the Microsoft Bot Framework and Bot Services.
Lessons
Introducing the Bot Service
Creating a Basic Chat Bot
Testing with the Bot Emulator
Module 3: Enhancing Bots with QnA Maker
The student will learn about the QnA Maker and how to integrate Bots and QnA Maker to build up a useful knowledge base for user interactions.
Lessons
Introducing QnA Maker
Implement a Knowledge Base with QnA Maker
Integrate QnA with a Bot
Module 4: Learn How to Create Language Understanding Functionality with LUIS
The student will learn about LUIS and how to create intents and utterances to support a natural language processing solution.
Lessons
Introducing Language Understanding
Create a new LUIS Service
Build Language Understanding with Intents and Utterances
Module 5: Enhancing Your Bots with LUIS
The student will learn about integrating LUIS with a Bot to better understand the users’ intentions when interacting with the Bot. Planning and Administering Microsoft Azure for SAP Workloads AZ-120
Lessons
Overview of language understanding for AI applications
Integrate LUIS and Bot to create an AI-based solution
Module 6: Integrate Cognitive Services with Bots and Agents
The student will learn about integrating Bots and Agents with Azure Cognitive Services for advanced features such as sentiment analysis, image and text analysis, and OCR and object detection.
Lessons
Understand Cognitive Services for Bot Interactions
Perform Sentiment Analysis for your Bot with Text Analytics
Detect Language in a Bot with the Language Cognitive Services
Integrate Computer Vision with Bots
