This post discusses Azure AI Search integrated vector embeddings for Retrieval Augmented Generation (RAG) when using a large language model for generative AI Q&A solutions, and provides steps to build a vector index using Azure AI Search.
This post explains how to use Fabric Semantic Link to leverage Power BI as a data source for Machine Learning model development in Fabric Data Science.
This post walks through the creation of a machine learning Jupyter notebook using IBM Watson Studio. The post illustrates the process step-by-step and includes an embedded video demo.
This post demonstrates how to use Retrieval Augmented Generation (RAG) to query OpenAI's Large Language Model (LLM) using a Vector database. This technique leverages the language understanding and summarization capabilities of Generative AI while introducing semantic understanding of our own data.
This post walks through the process of moving desktop-based Anaconda/Jupyter machine learning notebooks to Microsoft Fabric Synapse Data Science. The post illustrates the process and includes an embedded video demo.
This post discusses how Microsoft Azure OpenAI service employed a Retrieval Augmented Generation (RAG) technique called Use Your Data to enable Generative AI queries to incorporate private data. A full walk-through solution is included in text and video.
This video is a end-to-end tutorial showing how to deploy a Machine Learning model to a REST endpoint callable from another application via the web.
This post builds on the concepts in a previous post by exporting an Azure AI Custom Vision model to run within an iOS application.
This post walks through a sample solution using Microsoft Azure AI Vision. The resulting solution trains a model that can be used to detect packaging defects using AI Object Identification.