Generative AI Technical Patterns: Chat with Your Data
The "Chat with Your Data" architecture is more than just a technical solution; it is a path to empowering businesses with AI-augmented data interaction. It offers a robust and scalable way to process and retrieve information, combining the reliability of Azure Storage with the ingenuity of Azure AI. For clients who require a system that understands and responds to natural language queries with speed and accuracy, this architecture offers a compelling solution that will place them at the forefront of data accessibility and customer interaction.Technical Pattern: Build Your Own AI Assistant
The "Build Your Own AI Assistant" architecture is a highly adaptable and powerful solution that enables businesses to leverage AI to enhance their operations. It is designed with both the technical and business user in mind, ensuring that while it is underpinned by sophisticated technology, it remains accessible and practical for everyday use. Sales representatives can confidently position this architecture as a forward-thinking choice for organizations seeking to innovate and stay competitive by harnessing the power of AI for data-driven insights and tasks.
Build Your Feature Engineering System on AML Managed Feature Store and Microsoft Fabric
This article explains how to build a feature engineering system based on Azure Machine Learning managed feature store and Microsoft Fabric. It presents a scenario where a data scientist needs to access and transform data from various sources to train a model for predicting online vehicle service demand. It describes the architecture and data flow of the system, which uses Microsoft Fabric to orchestrate data pipeline and store data and models, Azure Machine Learning managed feature store to store and manage features, and Purview to track and monitor data lineage. It also provides some potential use cases and related resources for the feature engineering system.High-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 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.
