Microsoft Fabric includes a rich set of Data Science features, including Jupyter notebooks and the Synapse ML library, enabling scalable machine learning features. In this post we'll use Synapse ML to update an Azure AI Search index from a Jupyter notebook.
This video shows you how to use Azure AI sentiment analysis machine learning models within a Fabric Jupyter notebook to add user sentiment features to Data Lake tables.
We can leverage Azure AI services directly in Spark notebooks to enrich and transform data using Language AI Services. Learn how to use these powerful services using a Python Jupyter notebook stored in a Fabric workspace.
In this post learn how to build a Document Intelligence solution within Microsoft Fabric. This solution incorporates Azure AI to extract text from scanned documents. Using a Spark Notebook and Synapse ML, we can easily ingest scanned document data into a Data Lake solution.
Azure AI Document Intelligence can read images and PDF scans of forms, extracting data for later use in data solutions. While various language SDKs are available, it's also possible to call these services directly using the REST API. This tutorial walks through the REST API process.
My playlist of important AI practitioner focused sessions from Microsoft Ingnite 2023
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 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 post builds on the concepts in a previous post by exporting an Azure AI Custom Vision model to run within an iOS application.