This learning path will give you in-depth tutorials on adding artificial intelligence to your customer-facing applications. I’ve chosen two of the most common ways that companies add artificial intelligence to their customer interactions.

First, if you have a help desk, then there are probably a handful of questions that get asked much more frequently than others. If you could automate the responses to those questions, it would not only reduce your costs, but it would also let your help desk personnel focus on the more complex questions.

You will learn how to:

• Create a chatbot that can answer support questions about particular products or services
• Combine Azure Bot Service and Azure QnA Maker to do this
• Add speech input and output capabilities to a chatbot

The second use case is even more common. If your organization sells products, or even if it offers free items, such as news articles, then you can increase sales or customer engagement by adding recommendations for other items that your customers might want. Even if you already have a recommendation system, it might be possible to get better recommendations by building a more finely tuned model, which I’ll show you how to do.

You will learn how to:

• Implement the Microsoft Product Recommendations Solution on Azure
• Deploy and test the solution
• Fine-tune a recommendation model
• Evaluate the effectiveness of different models
• Make API calls to the recommendation system

Learning Objectives

• Create and configure an Azure QnA Maker knowledge base
• Create an Azure Bot Service chatbot that answers questions
• Deploy a recommendation engine on Azure
• Test and evaluate different recommendation models
• Make API calls to the Microsoft Product Recommendations Solutions

Intended Audience

• People who are interested in artificial intelligence services on Azure, especially chatbots and recommendation engines

Prerequisites

• Experience using Azure
• Experience writing code
• Experience using APIs