Concepts

In this tutorial, we will learn how to create a question-answering project using Microsoft Azure AI for designing and implementing a Microsoft Azure AI solution. Question-answering systems are widely used in various domains, including education, customer support, and information retrieval. By leveraging the power of Azure AI, we can build an efficient and accurate question-answering model.

Prerequisites

Before we begin, make sure you have the following:

  • An Azure account with access to the Azure Portal (Sign up for a free trial if you don’t have an account)

1. Set up a Knowledge Base

The first step is to create a knowledge base that will serve as the source of information for our question-answering system. Azure provides the QnA Maker service, which allows us to create and manage knowledge bases easily.

  1. Navigate to the Azure Portal and create a new QnA Maker resource.
  2. Follow the instructions to create a new knowledge base. You can import existing data or start from scratch.
  3. Once the knowledge base is created, you can add questions and their corresponding answers.

2. Train and Test the Knowledge Base

After setting up the knowledge base, it’s time to train and test it to ensure accurate answers to user queries.

  1. Use the QnA Maker service to train the knowledge base. It analyzes the data and extracts relevant information.
  2. Test the knowledge base by asking different questions. Verify if the system returns the expected answers.
  3. If the answers are not accurate, you can refine the knowledge base and retrain it.

3. Integrate the QnA Maker with Azure AI Services

Now that we have a trained knowledge base, let’s integrate it with Azure AI services to make it accessible to users.

  1. Create an Azure Cognitive Services resource in the Azure Portal.
  2. Enable the QnA Maker API within the Cognitive Services resource.
  3. Configure and connect the QnA Maker service to the Azure Cognitive Services resource.

4. Build a User Interface

To interact with the question-answering system, we need to create a user interface that allows users to input their questions and receive answers.

  1. Build a web or mobile application using your preferred framework. Here, we’ll use HTML and JavaScript for simplicity.
  2. Create an HTML form to capture user questions.
  3. Use JavaScript to send the user’s question to the QnA Maker service and display the answer.




Make sure to replace and with the appropriate values.

5. Deploy and Test

Deploy your application and test the question-answering functionality. Provide different questions and verify if the system returns accurate answers.

Congratulations! You have successfully designed and implemented a question-answering project using Microsoft Azure AI. You can further enhance the system by incorporating natural language processing techniques, training with more data, or integrating it with other Azure services.

Remember to explore the Microsoft documentation for more details and advanced features to make your question-answering system even more robust. Happy coding!

Answer the Questions in Comment Section

Which Azure service is commonly used for designing and implementing a question answering project?

  • a) Azure Machine Learning
  • b) Azure Cognitive Services
  • c) Azure Data Factory
  • d) Azure Stream Analytics

Correct answer: b) Azure Cognitive Services

Which Azure Cognitive Services API would you use to implement language understanding in a question answering model?

  • a) Speech to Text API
  • b) Text Analytics API
  • c) Language Understanding (LUIS) API
  • d) Computer Vision API

Correct answer: c) Language Understanding (LUIS) API

What are the main steps involved in designing and implementing a question answering project in Azure?

  • a) Data ingestion, pre-processing, training, and deployment
  • b) Data visualization, analysis, cleaning, and modeling
  • c) Data extraction, transformation, loading, and querying
  • d) Data classification, regression, clustering, and evaluation

Correct answer: a) Data ingestion, pre-processing, training, and deployment

Which of the following can be used to evaluate the performance of a question answering model?

  • a) Accuracy, precision, recall, and F1 score
  • b) Mean, median, mode, and standard deviation
  • c) AUC-ROC curve, lift curve, and calibration curve
  • d) Pearson correlation coefficient, Spearman’s rank correlation, and Kendall’s rank correlation

Correct answer: a) Accuracy, precision, recall, and F1 score

In Azure, which service can be used to extract insights and knowledge from large amounts of unstructured text?

  • a) Azure Text Analytics
  • b) Azure Machine Learning
  • c) Azure Cognitive Search
  • d) Azure Bot Service

Correct answer: a) Azure Text Analytics

Which Azure Cognitive Services API enables you to analyze and extract information from images in a question answering project?

  • a) Face API
  • b) Computer Vision API
  • c) Custom Vision API
  • d) Content Moderator API

Correct answer: b) Computer Vision API

Which Azure service allows for the creation of conversational AI experiences in a question answering project?

  • a) Azure Logic Apps
  • b) Azure Functions
  • c) Azure Bot Service
  • d) Azure Event Grid

Correct answer: c) Azure Bot Service

In a question answering project, what is the purpose of pre-processing the data?

  • a) To convert text data into numerical features
  • b) To filter out irrelevant information from the data
  • c) To split the data into training and testing sets
  • d) To label the data for supervised learning

Correct answer: b) To filter out irrelevant information from the data

Which Azure Cognitive Services API can be used to transcribe spoken language into written text in a question answering model?

  • a) Text Analytics API
  • b) Speech to Text API
  • c) Translator Text API
  • d) QnA Maker API

Correct answer: b) Speech to Text API

Which Azure service provides an easy way to create, train, and deploy machine learning models for a question answering project?

  • a) Azure Machine Learning
  • b) Azure Databricks
  • c) Azure Cognitive Services
  • d) Azure Data Lake Analytics

Correct answer: a) Azure Machine Learning

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Ronald Kuhn
1 year ago

Great blog post! I’m excited to start my project on AI-102 now.

Eren Sundberg
6 months ago

How did you handle data preprocessing in the question answering system?

Coşkun Çağıran

Thanks for the detailed guide. It has been really helpful!

Mudrolyub Barbon
7 months ago

I am stuck on integrating the QnA Maker with my existing database. Any pointers?

David Stojaković

The instructions were very clear. Kudos!

Carmel Steenbruggen
11 months ago

Can anyone explain the cost implications of using Azure Cognitive Services for this project?

Rosario Cortes
1 year ago

Just finished my project using this guide. Thanks a ton!

Jadranko Orlić
1 year ago

The implementation part was a bit confusing. Can anyone simplify it?

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