Select and include built-in skills for documents

It is essential to have the necessary skills and knowledge to leverage the available tools and technologies. This article will guide you through the process of selecting and including the built-in skills required for creating effective AI solutions on Azure. Let’s dive in!

1. Understanding Azure AI Capabilities

Azure provides a wide range of AI capabilities that can be integrated into your applications. These include natural language processing, speech recognition, computer vision, and more. Familiarizing yourself with these capabilities will help you determine the most suitable skills for your specific AI solution.

2. Identifying the Problem Statement

Before selecting the built-in skills, it is important to understand the problem you are trying to solve. Determine the key requirements and objectives of your AI solution. For example, if you are building a chatbot, you might need language understanding and generation skills to interact with users effectively.

3. Reviewing Cognitive Services

Azure Cognitive Services offer a collection of APIs and pre-trained models that can be easily integrated into your applications. Explore the various Cognitive Services available, such as Text Analytics, Face, and Speech, and select the ones that align with your solution’s requirements.

For instance, if you need to extract key phrases from a given text, the Text Analytics service can offer the necessary skill. You can implement it using the following code:

const keyPhrasesUrl = `${baseEndpoint}/text/analytics/v3.0/keyphrases`;

fetch(keyPhrasesUrl, {
method: "POST",
headers: {
"Ocp-Apim-Subscription-Key": subscriptionKey,
"Content-Type": "application/json"
},
body: JSON.stringify(document)
})
.then(response => response.json())
.then(response => {
console.log("Extracted key phrases:", response.documents[0].keyPhrases);
})
.catch(error => console.error(error));

4. Customizing with Azure Machine Learning

If the built-in Cognitive Services do not fully meet your requirements, you can leverage Azure Machine Learning to create custom models and skills. This allows you to train models using your own data and refine their performance based on your specific needs.

By using the AutoML capabilities in Azure Machine Learning, you can automate the process of training and deploying models. This snippet demonstrates how to train an image classification model:

const classificationDataset = azureMachineLearning.datasets.retrieve(
subscriptionId, resourceGroup, workspaceName, datasetName
);

const classificationModelName = classificationModel.name;

5. Integrating Azure Bot Service

For AI-driven customer interactions, Azure Bot Service provides an integrated environment to build conversational bots. It leverages the power of Azure AI and allows you to create intelligent and context-aware chatbots that can comprehend natural language and perform complex tasks.

By utilizing the Bot Framework SDK and Azure Bot Service, you can easily design, develop, and deploy bots. This sample code demonstrates creating a bot using Bot Framework SDK in C#:

public class GreetingBot : ActivityHandler
{
protected override async Task OnMessageActivityAsync(ITurnContext turnContext, CancellationToken cancellationToken)
{
// Get user message
var userMessage = turnContext.Activity.Text;

// Register the bot
BotFrameworkAdapter botAdapter = new BotFrameworkAdapter();
botAdapter.Use(new AuthorizationMiddleware());
botAdapter.ProcessAsync(Request, Response, botInstance);


In conclusion, designing and implementing a Microsoft Azure AI solution requires a thoughtful selection and integration of built-in skills. By understanding Azure AI capabilities, identifying the problem statement, reviewing Cognitive Services, customizing with Azure Machine Learning, and integrating Azure Bot Service, you can build effective AI solutions tailored to your specific requirements. Happy coding!