With the advent of AI and the datasets available, we can now build a sophisticated Question Answering (QA) system. These QA systems are massive information retrieval tools that can give precise answers to specific questions. Such systems are used in various interfaces such as customer service bots, help desk systems, and more. One such service is Microsoft’s Azure AI Solution.
For this tutorial, we will use the Azure AI-102, which is designed to produce skilled AI professionals proficient in planning and implementing Azure AI solutions.
I. Understanding the Need for a QA Project
Before moving on with the project, it is vitally important to understand the necessity of a question-answering project. It aims to build an AI system that can not only handle large volumes of data but can also unearth precise bits of information in response to specific question triggers.
II. Pre-Requisites to Build the Project
Building a QA project involves a step-by-step process, and before jumping onto the project, you should arrange some mandatory components:
- An Azure account with an active subscription.
- Installed and configured Azure CLI.
- Basic understanding of Azure services.
III. Steps to Start the QA Project
Step 1: Setting up the environment
Just like any other system, first, we need to set up the environment before starting. Create a resource group using the Azure CLI with the following command:
az group create --name myResourceGroup --location "West Europe"
Step 2: Creating a base model
The base model made should contain all frequently asked questions with their respective answers. We can create the base model using ‘QnA Maker service.’ It uses an existing FAQ page or adds a manual question-answer pair.
Step 3: Training and publishing the model
Train the model using the ‘Test’ panel where you can enter a query and check the response of your model. If the model provides a relevant answer, save and train the model.
Step 4: Consuming the model
To consume the model by the end-user, use HTTPS endpoint after publishing the model. One can use Azure Function or Azure Logic App to consume the model.
IV. Tips to Improve the Service
- Train your model frequently with the new queries.
- Always update the knowledge base with the new FAQs.
- Use active learning where you receive suggestions to improve the model.
Through this article, we have only scratched the surface of building a question-answering project. Microsoft AI-102 offers a more hands-on understanding and additional resources to implement intelligent AI solutions. Do remember to keep learning and evolving while building your models and networks. The future is AI, and with tools and resources like Azure, shaping the future becomes more comfortable than ever.
Practice Test
True or False: Azure Cognitive Service provides APIs, SDKs, and services that can be used in a question-answering project.
- True
- False
Answer: True
Explanation: Azure Cognitive Service does provide APIs, SDKs, and services to help developers build intelligent applications without having direct AI or data science skills or knowledge.
Which one of these is an essential service to utilize for a question answering project in Azure?
- a) LUIS
- b) QnA Maker
- c) Text Analytics
- d) Bing Spell Check
Answer: b) QnA Maker
Explanation: QnA Maker is an Azure Cognitive Service that facilitates the creation, maintenance, and deployment of the knowledge bases filled with questions and answers.
In the context of Azure AI Solution, what does the abbreviation ‘LUIS’ stands for?
- a) Language Understanding Intelligence Service
- b) Linguistic User Interface Software
- c) Logic Underlying Intelligence Service
- d) Language Utilities Integration Service
Answer: a) Language Understanding Intelligence Service
Explanation: LUIS stands for Language Understanding Intelligence Service in the context of Microsoft Azure, which is part of the Azure Cognitive Services.
True or False: In a question answering project using the QnA Maker, you cannot update the knowledge base once created.
- True
- False
Answer: False
Explanation: The QnA Maker allows modification and updating of the knowledge base as and when required.
Multiple Selection: Which Azure services can be used together to build a question answering bot?
- a) Azure Bot Service
- b) Azure Functions
- c) Azure Machine Learning
- d) Azure Data Lake Storage
Answer: a) Azure Bot Service, b) Azure Functions, and c) Azure Machine Learning
Explanation: A combination of Azure Bot Service, Azure Functions, and Azure Machine Learning can be used to build an intelligent, scalable question answering bot.
True or False: A single knowledge base in QnA Maker can support multiple languages.
- True
- False
Answer: True
Explanation: A single knowledge base in QnA Maker can indeed support multiple languages.
Which Azure service is used to create conversational AI solutions, like question answering bots?
- a) Azure Bot Service
- b) Azure Data Factory
- c) Azure Logic Apps
- d) Azure DevOps
Answer: a) Azure Bot Service
Explanation: Azure Bot Service is specifically designed for building and deploying high-quality bots for interactive, conversational experiences.
True or False: QnA Maker API requires programming skills to be used.
- True
- False
Answer: True
Explanation: To use QnA Maker API, one needs to have programming skills as APIs are programmed to interact with other software.
Multiple Selection: The knowledge bases created in QnA Maker can be sourced from which types of documents?
- a) Word documents
- b) PDF files
- c) Text files
- d) Excel spreadsheets
Answer: a) Word documents, b) PDF files, and c) Text files
Explanation: QnA Maker supports ingestion of text from Word documents, PDF files and Text files to create the knowledge bases.
True or False: You can test your knowledge base directly in the QnA Maker portal.
- True
- False
Answer: True
Explanation: You can indeed test your knowledge base directly in the QnA Maker portal using the ‘Test’ function.
What is the primary purpose of Azure Bot Service in the context of a question answering project?
- a) Storage service
- b) Compute service
- c) Scalable deployment of bots
- d) Data processing
Answer: c) Scalable deployment of bots
Explanation: Azure Bot Service is used for the creation, management and scalable deployment of intelligent, conversational bots.
True or False: The Azure Bot Service provides integrated environment for bot development without having to provision and manage servers.
- True
- False
Answer: True
Explanation: Azure Bot service indeed provides an integrated environment where bot development can be carried out efficiently without server management tasks.
In LUIS, the model’s ability to understand user utterances is determined by what?
- a) Intentions
- b) Entities
- c) Phrase lists
- d) Features
Answer: a) Intentions
Explanation: Intentions help LUIS understand user commands and hence determine the ability to comprehend user utterances.
True or False: The published version of the LUIS application can be updated without affecting the end users.
- True
- False
Answer: False
Explanation: Any changes in the published version of a LUIS application can potentially affect the end users, since the changes would reflect in the application’s responses.
In context of creating an Azure AI Solution, what does ‘AI’ stand for?
- a) Aggregated Intelligence
- b) Artificial Inputs
- c) Applied Immersion
- d) Artificial Intelligence
Answer: d) Artificial Intelligence
Explanation: In the given context, AI refers to Artificial Intelligence, a field of computer science that works on creating systems capable of performing tasks that require human intelligence.
Interview Questions
What are the major aspects to consider when designing and implementing a Microsoft Azure AI solution?
The major aspects to consider include defining and implementing data models, developing AI models for the solution, deploying the AI models into production, and handling incoming requests to the AI service.
What tools are involved in the AI project implementation in Microsoft Azure?
The tools involved are Azure Machine Learning Service, Azure Databricks, Azure Cognitive Search, Azure Bot Service, Microsoft Power BI, and Azure Cognitive Services.
Can you define what Azure Machine Learning Service is?
Azure Machine Learning service is a cloud service that you can use to develop, train, test, deploy, manage, and track machine learning models.
What functions does Azure Databricks serve in creating an AI Project?
Azure Databricks can be used for large scale data engineering and exploratory data analysis, as well as the development of complex machine learning models.
How can Azure Cognitive Search be used in a question answering project?
Azure Cognitive Search is a cloud search service which includes built-in Artificial Intelligence (AI) capabilities that can be used to ingest, enrich, and explore structured and unstructured data, enhancing the project’s ability to provide robust, contextual answers.
What purpose does Azure Bot Service serve in the context of an AI project?
Azure Bot Service is a platform for creating, publishing, and managing intelligent, enterprise-grade bots. These bots can be integrated with various channels to interact with users using natural language and provide automated services.
What is the role of Microsoft Power BI in creating a question answering project on Azure AI?
Microsoft Power BI is a business analytics tool that can be used for visualizations, report creation, and data analysis in an AI project. It can be used to create interactive and real-time dashboards for monitoring the performance of an AI model.
What is Azure Cognitive Services?
Azure Cognitive Services is a collection of AI services and cognitive APIs to help developers build intelligent applications without having direct AI or data science knowledge or skills.
What do you understand by ‘Deploying an AI model’?
Deploying an AI model in the context of Azure essentially involves publishing the trained AI model as a web service, either on Azure Container Instances for development and testing, or on Azure Kubernetes Service for scalable, production-grade deployment.
How can you handle incoming requests to the AI service in an AI Project?
Incoming requests to the AI service can be managed by leveraging Azure API Management service which helps in securing, monitoring, and managing APIs in a scalable environment.
What is the purpose of data models in Azure AI?
Data models define the schema for data that will be used in a machine learning project. They can be used to train, validate, and score models in Azure Machine Learning.
How do you develop the AI models for the solution?
You can develop AI models by using Azure Machine Learning designer or writing Python scripts using Azure Machine Learning SDK.
How can you monitor the performance of an AI model in Azure?
Azure provides monitoring tools such as Azure Monitor and Azure Application Insights to track the usage and performance of an AI model.
What is the role of ‘Azure Machine Learning Studio’ in Azure AI project?
Azure Machine Learning Studio is a browser-based tool that allows you to build, test, and deploy machine learning models using drag-and-drop functionality. It simplifies the process of building ML models for end-users, especially those without extensive programming knowledge.
What are the main components of AI model lifecycle in Azure?
The main components of the AI model lifecycle in Azure are model design, data ingestion and preprocessing, model training and validation, model deployment, and model monitoring and management.
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