In this session, we will explore the Azure AI Language conversational language understanding service (CLU) and its capabilities in building intelligent models that can extract meaning from natural language. Attendees will learn how to train, test, and deploy a conversational model that can understand user inputs and provide relevant responses. This session covers essential topics such as defining intents, using patterns to differentiate similar utterances, and leveraging pre-built entity components. Practical examples and hands-on exercises will give participants valuable experience in setting up, testing, and refining a conversational model using Azure AI’s capabilities.

Read More - https://aka.ms/AILanguage

How do you think a Start-up will be benefited from this session?
Start-ups can greatly benefit from incorporating conversational language understanding into their applications. This session demonstrates how to utilize Azure AI CLU to create models that can handle customer interactions, automate support, and improve user engagement. Attendees will learn best practices for defining intents, using patterns, and leveraging pre-built entities, enabling them to develop efficient and effective conversational AI solutions. This session equips start-ups with the knowledge to deploy scalable AI-driven solutions that adapt to evolving business needs and enhance customer experience.

The session will focus on:
Understanding the prebuilt capabilities of the Azure AI Language service: An overview of the language understanding features and their applications in modern conversational models.
Understanding resources for building a conversational language understanding model: Setting up and configuring Azure resources to support conversational AI.
Defining intents, utterances, and entities: How to specify the purpose of a conversation, the words that users might say, and the entities they may refer to.
Using patterns to differentiate similar utterances: Techniques for managing variations in user input to ensure accurate understanding.
Using pre-built entity components: Leveraging existing entities like dates, times, numbers, and places for enhanced language understanding.
Training, testing, publishing, and reviewing a conversational language understanding model: Step-by-step process for setting up and refining a conversational model, including evaluation and deployment.
Hands-on Exercise: Participants will practice building and testing a conversational model in Azure, focusing on creating intents, entities, and utilizing patterns to handle different utterances.

What will the attendees or a Start-up learn from the session?
Attendees, including start-ups, will gain the following:
Skills for setting up Azure resources necessary for building conversational language models.
Techniques for defining and managing intents, utterances, and entities to tailor the model’s understanding.
How to use patterns to manage varied user inputs and ensure the model's accuracy.
Practical experience with Azure AI CLU to train, test, and deploy conversational models.
Knowledge of pre-built entity components that simplify the model-building process and enhance understanding.
Best practices for evaluating and refining a conversational model to improve performance and user experience.

Speaker BIO- Viswanatha Swamy
He is an aspirant Software Architect and currently, he works at Applied Information Sciences. He is passionate about C#, ASP.NET, Azure, Performance testing/tuning,.NET Core, and Docker. He loves to learn about new technologies.

Social Handle- https://twitter.com/vishipayyallore