Getting started with the NetApp Connector for Microsoft Copilot and Azure NetApp Files
Imagine a world where your on-premises and enterprise cloud files seamlessly integrate with Microsoft Copilot unleashing AI on your Azure NetApp Files enterprise data, and making your workday smoother and more efficient. Welcome to the future with the NetApp Connector for Microsoft Copilot!Building scalable and persistent AI applications with LangChain, Instaclustr, and Azure NetApp Files
Discover the powerful combination of LangChain and LangGraph for building stateful AI applications and unlock the benefits of using a managed-database service like NetApp® Instaclustr® backed by Azure NetApp Files for seamless data persistence and scalability.
Transform Insurance Industry Workflows Using Generative AI Models and Azure Services
This article highlights an innovative automated solution designed to transform the processing of insurance claim forms for the insurance industry. Previously, underwriters were limited to handling just two to three claims per day, significantly hampering operational efficiency. With the implementation of this solution, companies have achieved a remarkable 60% increase in daily claim processing capacity. Built on Azure services, this architecture revolutionizes the management of claim forms submitted via email by automating critical tasks such as data extraction, classification, summarization, evaluation, and storage. Leveraging the power of AI and machine learning, this solution ensures faster, more accurate claim evaluations, enabling insurance companies to make informed decisions efficiently. The result is enhanced operational scalability, improved customer satisfaction, and a streamlined claims process. Scenario In the insurance industry, claim forms often arrive as email attachments, requiring manual processing to classify, extract, and validate information before it can be stored for analysis and reporting. This solution automates the process by leveraging Azure services to classify, extract, and summarize information from Insurance claim forms. Using Responsible AI evaluation, it ensures the performance of Large Language Models (LLMs) meets high standards. The data is then stored for further analysis and visualization in Power BI, where underwriters can access consumable reports. Architecture Diagram Components Azure Logic Apps: Automates workflows and integrates apps, data, and services. Used here to process emails, extract PDF attachments, and initiate workflows with an Outlook connector for attachment, metadata, and email content extraction. Azure Blob Storage: Stores unstructured data at scale. Used to save insurance claim forms in PDF and metadata/email content in TXT formats. Azure Functions: Serverless compute for event-driven code. Orchestrates workflows across services. Azure Document Intelligence: AI-powered data extraction from documents. Classifies and extracts structured content from ACCORD forms. Azure OpenAI: Provides advanced language models. Summarizes email content for high-level insights. LLM Evaluation Module (Azure AI SDK): Enhances Azure OpenAI summaries by evaluating and refining output quality. Azure AI Foundry: Manages Azure OpenAI deployments and evaluates LLM performance using Responsible AI metrics. Azure Cosmos DB: Globally distributed NoSQL database. Stores JSON outputs from Azure OpenAI and Document Intelligence. Microsoft Power BI: Visualizes Cosmos DB data with interactive reports for underwriters. Workflow Description The workflow for processing claims efficiently leverages a series of Azure services to automate, structure, and analyze data, ensuring a fast, accurate, and scalable claims management system. 1. Email Processing with Azure Logic Apps The process begins with a pre-designed Azure Logic Apps workflow, which automates the intake of PDF claim forms received as email attachments from policyholders. By using prebuilt Outlook connectors, it extracts key details like sender information, email content, metadata, and attachments, organizing the data for smooth claims processing. This automation reduces manual effort, accelerates claim intake, and minimizes data capture errors. 2. Secure Data Storage in Azure Blob Storage Once emails are processed, the necessary PDF attachments, email content, and email metadata are stored securely in Azure Blob Storage. This centralized, scalable repository ensures easy access to raw claim data for subsequent processing. Azure Blob’s structured storage supports efficient file retrieval during later stages, while its scalability can handle growing claim volumes, ensuring data integrity and accessibility throughout the entire claims processing lifecycle. 3. Workflow Orchestration with Azure Functions The entire processing workflow is managed by Azure Functions, which orchestrates serverless tasks such as document classification, data extraction, summarization, and LLM evaluation. This modular architecture enables independent updates and optimizations, ensuring scalability and easier maintenance. Azure Functions streamlines operations, improving the overall efficiency of the claims processing system. a. Document Classification: The next step uses Azure Document Intelligence to classify documents with a custom pretrained model, identifying insurance claim forms. This step ensures the correct extraction methods are applied, reducing misclassification and errors, and eliminating much of the need for manual review. The ability to customize the model also adapts to changes in document formats, ensuring accuracy and efficiency in later processes. b. Content Extraction: Once the insurance form is properly classified, Azure Document Intelligence extracts specific data points from the PDF claim forms, such as claim numbers and policyholder details. The automated extraction process saves time, reduces manual data entry, and improves accuracy, ensuring essential data is available for downstream processing. This capability also helps in organizing the information for efficient claim tracking and report generation. c. Document Intelligence Output Processing: The results are extracted in JSON format and then parsed and organized for storage in Azure Cosmos DB, ensuring that all relevant data is systematically stored for future use. d. Summarizing Content with Azure OpenAI: Once data is extracted, Azure OpenAI generates summaries of email content, highlighting key claim submission details. These summaries make it easier for underwriters and decision-makers to quickly understand the essential points without sifting through extensive raw data. e. Quality Evaluation with LLM Evaluation SDK: After summarization, the quality of the generated content is evaluated using the LLM Evaluation Module in the Azure AI SDK. This evaluation ensures that the content meets accuracy and relevance standards, maintaining high-quality benchmarks and upholding responsible AI practices. Insights from this evaluation guide the refinement and improvement of models used in the workflow. f. LLM Performance Dashboard with Azure AI Foundry: Continuous monitoring of the workflow’s quality metrics is done via the evaluation dashboard in Azure AI Foundry. Key performance indicators like Groundedness, fluency, coherence, and relevance are tracked, ensuring high standards are maintained. This regular monitoring helps quickly identify performance issues and informs model optimizations, supporting the efficiency of the claims processing system. g. Summarization Output Processing: After evaluation, the results from the OpenAI summarization output are parsed and stored in Cosmos DB, ensuring that all relevant data is saved in a structured format for easy access and retrieval. 4. Storing Data in Azure Cosmos DB The structured data, including parsed JSON outputs and summaries, is stored in Azure Cosmos DB, a fully managed, globally distributed NoSQL database. This solution ensures processed claim data is easily accessible for further analysis and reporting. Cosmos DB’s scalability can accommodate increasing claim volumes, while its low-latency access makes it ideal for high-demand environments. Its flexible data model also allows seamless integration with other services and applications, improving the overall efficiency of the claims processing system. 5. Data Visualization with Microsoft Power BI The final step in the workflow involves visualizing the stored data using Microsoft Power BI. This powerful business analytics tool enables underwriters and other stakeholders to create interactive reports and dashboards, providing actionable insights from processed claim data. Power BI’s intuitive interface allows users to explore data in depth, facilitating quick, data-driven decisions. By incorporating Power BI, the insurance company can effectively leverage stored data to drive business outcomes and continuously improve the claims management process. Related Use cases: Healthcare - Patient Intake and Medical Claims Processing: Automating the extraction and processing of patient intake forms and medical claims for faster reimbursement and improved patient care analysis. See the following article for more information on how to implement a solution like this. Financial Services - Loan and Mortgage Application Processing: Streamlining loan application reviews by automatically extracting and summarizing financial data for quicker decision-making. Retail - Supplier Invoice and Purchase Order Processing: Automating invoice and purchase order processing for faster supplier payment approvals and improved financial tracking. Legal contract and Document Review: Automating the classification and extraction of key clauses from legal contracts to enhance compliance and reduce manual review time. See the following article for more information on how to implement a solution like this. Government - Tax Filing and Documentation Processing: Automating the classification and extraction of tax filing data to ensure compliance and improve audit efficiency. To find solution ideas and reference architectures for Azure based solutions curated by Microsoft, go to the Azure Architecture Center and search with keywords like “retail”, “legal”, “healthcare”, etc. You’ll find hundreds of industry-related solutions that can help jumpstart your design process. Contributors: This article is maintained by Microsoft. It was originally written by the following contributors. Principal authors: Manasa Ramalinga| Principal Cloud Solution Architect – US Customer Success Oscar Shimabukuro Kiyan| Senior Cloud Solution Architect – US Customer SuccessHigh-performance storage for AI Model Training tasks using Azure ML studio with Azure NetApp Files
This article describes how to provide enterprise grade high performance persistent storage with data protection capability for AI Model training tasks using studio compute instances with Azure NetApp Files (ANF).Distributed ML Training for Click-Through Rate Prediction with NVIDIA, Dask and Azure NetApp Files
The work of a data scientist should be focused on the training and tuning of machine learning (ML) and artificial intelligence (AI) models. However, according to research, data scientists spend approximately 80% of their time figuring out how to make their models work with enterprise applications and run at scale. This article will help shift that paradigm.Distributed ML Training for Lane Detection, powered by NVIDIA and Azure NetApp Files
Microsoft, NetApp and Run:ai have partnered in the creation of this article to demonstrate the unique capabilities of the Azure NetApp Files together with the Run:ai platform for simplifying orchestration of AI workloads. This article provides a reference architecture for streamlining the process of both data pipelines and workload orchestration for Distributed Machine Learning Training for Lane Detection, by ensuring the use of the full potential of NVIDIA GPUs.
