Content is currently being validated and refreshed as of Aug 2019.

In this lab, we will introduce our case study and setup tools on your local workstation and in your Azure instance to enable you to build tools within the Microsoft Cognitive Services suite.

• case study
• set up

This hands-on lab guides you through creating an intelligent console application from end-to-end using Cognitive Services (specifically the Computer Vision API). We use the ImageProcessing portable class library (PCL), discussing its contents and how to use it in your own applications.

• introduction
• hands-on

In this lab, we will be setting up an intelligent bot from end-to-end that can respond to a user's chat window text prompt. We will be building on what we have already learned about building bots within Azure, but adding in a layer of custom logic to give our bot more bespoke functionality.

• introduction
• hands-on

In the previous lab, we built a chat bot and modified the downloaded code to suit out needs. Now, we wish to log bot chats to enable our customer service team to follow up to inquiries, determine if the bot is performing in the expected manner, and to analyze customer data.

• introduction
• hands-on

QnA Maker provides a conversational question and answer layer over your data. This allows your bot to send QnA Maker a question and receive an answer without you needing to parse and interpret the intent of their question.

• introduction
• hands-on

We're going to build an end-to-end scenario that allows you to pull in your own pictures, use Cognitive Services to find objects and people in the images, and obtain a description and tags. We'll later build a Bot Framework bot using LUIS to allow easy, targeted querying.

• introduction
• hands-on

In this hands-on our bot will be capable of taking in a user's input and responding based on the user's input, we will give our bot the ability to understand natural language with the LUIS model we built in previous exercise.

• introduction
• hands-on

In this hands-on we implement Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis. The Language Detection feature can detect a wide range of languages, variants, dialects, and some regional or cultural languages. The exact list of languages for this feature isn't published. Those capabilities will be added to the bot developed before.

• introduction
• hands-on

This hands-on lab guides you through some of the basics of testing bots. This workshop demonstrates how you can perform functional testing (using Direct Line).

• introduction
• hands-on