Conversation Language Understanding Model (Part 2)

By Ashwin Venugopal -

 




Store Your Data

Language Studio and Azure AI Services both provide access to Azure Blob storage. The easiest option is to select Container as the container must be available, although private containers can also be used with some extra setup.

You also need a method for classifying each document in addition to your data. You can manually categorize each document one at a time using the graphical interface that Language Studio offers.

There are two types of projects available, if a document belongs to a single class, utilize a single label categorization project. Use the multi-label categorization project if you are able to map a document to multiple classes.

If you don't want to manually classify each document, you can label all your documents before you create your Azure AI Language project.

Create Your Azure AI Language Project

Your Azure AI Language project can be created in one of two ways. Language Studio will offer to build a language service for you if you begin using it without first creating one in the Azure portal.

The most flexible way to create a Azure AI Language project is to first create your language service using the Azure portal. If you choose this option, you get the option to add custom features.

Choose that custom feature while building your language service, just as you were going to construct a custom text classification. Using this procedure, you will also connect the language service to a storage account.

You can go straight to the Language Studio from the language service's overview pane after the resource has been deployed. After that, you can start a brand-new, unique text classification project.

Conclusion

We have successfully learnt about storing data and creating Azure AI language project.












Comments

Post a Comment

Popular posts from this blog

Connect Data to Azure Sentinel Using Data Connectors

By Ashwin Venugopal -
Image
  Ingest Log Data with Data Connectors You should connect your data sources with Azure Sentinel Connectors to collect the log data whose page displays a growing list of the connectors provided by Azure Sentinel, after selecting the Open connector page (the detailed connector page has a left and right blade).  The information about the connector, the connector's status, and the last time a log was received if connected is provided by the left blade. Whereas the right tab has two tabs- Instructions and Next Steps. The instructions tab can be different based on the connector having Prerequisites and Configuration which can be followed to connect to the data source. The Next Steps tab offers a quick reference to workbooks, query samples, and analytical templates. Data connectors can only be disconnected/deactivated, not deleted. Note-  The connector does not install Workbooks and Analytical Templates as they are already available in the Sentinel environment for out of the box...
Read more

Information Protection Scanner: Resolve Issues with Information Protection Scanner Deployment

By Ashwin Venugopal -
Image
  About If you are experiencing problems with the Microsoft Purview Information Protection scanner, check the health of your deployment by utilizing the Start-ScannerDiagnosticsPowerShell cmdlet to start the scanner diagnostic tool. The diagnostics tool checks the following details and then creates a log files with the results: Whether the database is up to date. Whether network URLs are accessible. Whether there's a valid authentication token and policy can be acquired. Whether the profile is defined in the Azure portal. Whether offline/online configuration exists and can be acquired. Whether the rules configured are valid.  The Start-ScannerDiagnostics command does not perform a comprehensive check of prerequisites. If you encounter problems with the scanner, you need to verify that your system meets the scanner's requirements, and that your scanner's configuration and installation are finished.  Verify Scanning Details Per Scanner Node and Repository Run the Get-ScanSt...
Read more

Azure AI Search plugin in Microsoft Security Copilot (Preview)

By Ashwin Venugopal -
Image
  Introduction Azure AI Search is a service that allows to reliably extract content search, extract, and understand at scale. It is typically used to search for documents, data surveys, and chat-style Copilot apps over proprietary data. Indexing and querying are Azure AI Search's two main workloads. While querying allows to search the content from index, indexing loads the content into the search engine and makes it searchable. By connecting to one of the Azure AI Search indexes from within Copilot, the Microsoft Security Copilot integration for Azure enables to use the content in Copilot to produce contextualized, pertinent, and targeted responses.  Requirement Before using the Azure AI Search plugin in Copilot, the desired Azure AI Search index to connect to Copilot is configured as follows: The text field must be searchable.  The title field must be filterable. The vector-field must use text-embedding-ada-002. Integrated vectorization can be used to setup an index in t...
Read more