Azure Bot Service — Microsoft Bot Framework, Channels, and Cognitive Services

Microsoft Bot Framework

Microsoft Bot Framework is an integrated environment for bot development which is provided by Azure Bot Service. Azure Bot Service helps us to build, connect, test, deploy, and manage bots. Azure Bot Service provides the Bot Framework SDK with support for C# and JavaScript.

Components of Bot Framework

Azure Bot Service Channels

Social media endpoints are always changing. Developing for one social media platform is just not cost-effective.

Azure Bot service channels help to develop the core business features for Bot once using cognitive services but deploy it to new social media platforms without much changes to the core bot features. At an equivalent time, core bot features are often improved constantly, and these improvements will automatically benefit the already existing channels through which users interact with bots.

Here are a number of social media platform that acts as a channel for Azure Bot Services:

This messaging platform acts as the channel between the user and the Bot Framework cognitive services. Through the middleware created by the Bot Builder Community, Bots can reach channels such as Alexa and Google assistance.

Cognitive Services

Azure Cognitive Services are used to include the ability to hear, speak, search, see, speak, understand, and accelerate decision-making in your application by providing API.

Some of Cognitive Services Used for AI

Language

  • Language Understanding (LUIS) is a cloud-based API service.
  • Language Understanding provides custom machine learning algorithms to predict user’s conversational language input and provides relevant details as an output
  • A client application for LUIS is any conversational messaging application that communicates with a user in their conversational, natural language to complete a task.
  • Client application examples — social media apps, chatbots, and speech-enabled desktop applications.

QnA Maker

  • It provides a cloud-based Natural Language Processing (NLP) service.
  • It creates a natural conversational layer over the application data and it will use this data to find the most appropriate answer for any given user input, from trained application data.
  • Client application examples — social media apps, chatbots, and speech-enabled desktop applications.

Speech

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