Module 1: Introducing Azure Cognitive Services
The student will learn about the available Cognitive Services on Microsoft Azure and their role in architecting AI
- 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.
- 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.
- 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
- 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 usersâ€™ intentions when
interacting with the Bot.
- 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.
- 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
An Azure AI engineer works with Data Engineers and Data Scientists to analyze requirements for AI
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.
This course is aimed at Cloud Solution Architects, Azure artificial intelligence designers, and AI developers.