Train Your Own Enterprise Data with Azure OpenAI Service and ChatGPT

🔍 Azure OpenAI Service allows training custom Enterprise data and deploying it to a website.

💻 Creating a Resource Group and Azure Open AI resource is the first step.

🌐 Accessing and launching the Open AI Studio is essential for utilizing the Azure OpenAI Service.

🔑 Create an Azure OpenAI resource and deploy the GPT 3.5 Turbo model.

💡 Add data sources, such as Azure blob storage, to train the model with custom data.

📚 Upload files, like Word documents, to the storage account in Blob container for training.

📦 Uploading a file to a container and waiting for cognitive search to finish.

🔍 Selecting blob storage and cognitive search results in Open AI Studio.

🌐 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.

🔒 Encryption is the process of converting plain text into coded messages to protect sensitive information.

📁 File encryption can be used in secure messaging apps to ensure data privacy.

💻 Azure OpenAI Service allows you to train your own enterprise data and deploy it to a web application.

📚 Azure OpenAI Service allows users to train their own enterprise data.

🌐 The web application created with Azure OpenAI Service has a basic user interface and allows API calls to interact with custom data.

💾 Azure OpenAI Service supports various file types for data upload, including text, markdown, HTML, Word, PowerPoint, and PDF.

🔍 The video discusses how to train your own enterprise data with Azure OpenAI service.

👥 It explains how to enable or disable authentication on a web app and how to support multi-tenant applications.

🚀 The video also mentions the benefits of incremental live streaming for handling large data and showcases the chat interface with user state maintenance.