π Azure OpenAI Service allows training custom Enterprise data and deploying it to a website.
π» Creating a Resource Group and Azure Open AI resource is the first step.
π Accessing and launching the Open AI Studio is essential for utilizing the Azure OpenAI Service.
π Create an Azure OpenAI resource and deploy the GPT 3.5 Turbo model.
π‘ Add data sources, such as Azure blob storage, to train the model with custom data.
π Upload files, like Word documents, to the storage account in Blob container for training.
π¦ Uploading a file to a container and waiting for cognitive search to finish.
π Selecting blob storage and cognitive search results in Open AI Studio.
π Configuring authentication for a web application and deploying it.
The video demonstrates how to train your own enterprise data with Azure OpenAI Service for custom chat responses.
The process involves importing data to a storage account, creating an index blog, and generating vectors.
Best practices for cyber security, such as strong passwords and multi-factor authentication, are discussed.
π Encryption is the process of converting plain text into coded messages to protect sensitive information.
π File encryption can be used in secure messaging apps to ensure data privacy.
π» Azure OpenAI Service allows you to train your own enterprise data and deploy it to a web application.
π Azure OpenAI Service allows users to train their own enterprise data.
π The web application created with Azure OpenAI Service has a basic user interface and allows API calls to interact with custom data.
πΎ Azure OpenAI Service supports various file types for data upload, including text, markdown, HTML, Word, PowerPoint, and PDF.
π The video discusses how to train your own enterprise data with Azure OpenAI service.
π₯ It explains how to enable or disable authentication on a web app and how to support multi-tenant applications.
π The video also mentions the benefits of incremental live streaming for handling large data and showcases the chat interface with user state maintenance.
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