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Connecting on-premises data to the Azure platform for machine learning and generative AI Retrieval Augmented Generation (RAG) involves several steps. Here’s a step-by-step guide: Step 1: Set Up Azure Machine Learning Workspace 1. Create an Azure Machine Learning Workspace: This is your central place for managing all your machine learning resources. 2. Configure Managed Virtual Network: Ensure your workspace is set up with a managed virtual network for secure access to on-premises resources. Step 2: Establish Secure Connection 1. Install Azure Data Gateway: Set up an Azure Data Gateway on your on-premises network to securely connect to Azure. 2. Configure Application Gateway: Use Azure Application Gateway to route and secure communication between your on-premises data and Azure workspace. Step 3: Connect On-Premises Data Sources 1. Create Data Connections: Use Azure Machine Learning to create connections to your on-premises data sources, such as SQL Server or Snowflake - Azure Machine ....

Data Ingestion for Retrieval-Augmented Generation (RAG) Data Ingestion is a critical initial step in building a robust Retrieval-Augmented Generation (RAG) system. It involves the process of collecting, cleaning, structuring, and storing diverse data sources into a format suitable for efficient retrieval and generation. Key Considerations for Data Ingestion in RAG: Data Source Identification: Internal Data: Company documents, reports, knowledge bases, customer support tickets, etc. Proprietary databases, spreadsheets, and other structured data. External Data: Publicly available datasets (e.g., Wikipedia, Arxiv) News articles, blog posts, research papers from various sources Social media data (with appropriate ethical considerations) Data Extraction and Cleaning: Text Extraction: Extracting relevant text from various formats (PDF, DOCX, HTML, etc.) Data Cleaning: Removing noise, inconsistencies, and irrelevant information Normalization: Standardizing text (e....