12 videos
9:35
This video walks through the process of calling Azure AI Document Intelligence services using the REST API, enabling viewers to extract data from images and PDF scans of forms directly via API calls.
5:42
This video walks through the process of integrating Azure AI Translation into a Fabric Data Lake solution using Synapse ML features. Viewers will learn how to apply Language AI Services within Python Jupyter notebooks stored in a Fabric workspace for data enrichment and transformation.
15:31
This video walks through the process of building a Document Intelligence solution using Azure AI within Microsoft Fabric. It demonstrates how to ingest scanned document data into a Data Lake solution by leveraging Spark Notebook and Synapse ML for text extraction.
19:11
This video walks through the process of deploying a machine learning model to a REST endpoint using Microsoft Azure Machine Learning, covering all necessary steps from creating resources and launching ML Studio to training the model and registering it before finally deploying the inference endpoint. Viewers will learn how to integrate their trained model into production applications by making predictions available via HTTP requests.
6:23
This video walks through the process of exporting a machine learning model developed in Microsoft Fabric, importing it into an Azure Machine Learning workspace, and deploying it to a real-time inference endpoint. Viewers will learn how to set up and test this deployment, ensuring their models can provide real-time predictions efficiently.
8:16
This video walks through the process of selecting pre-trained models from Azure ML's Model Catalog and deploying them on Azure's computing services, culminating in accessing these models via a REST API. Viewers will learn how to create a resource group, set up a workspace, and test an endpoint.
11:34
This video walks through the process of using Azure AI Vision to generate descriptive metadata for images, and then integrating this data into a Power BI analysis solution. Viewers will see a live demonstration of creating an enriched image dataset and how it can be visualized in Power BI.
13:48
This video walks through the process of exporting AI Vision models from Azure cloud services and integrating them into iOS applications using CoreML. Viewers will learn how to test, export, and run these models locally on iOS devices, enhancing their understanding of edge computing in AI applications.
10:50
This video walks through the process of creating an Azure AI Custom Vision object detection model to identify packaging defects in products. Viewers will see how to set up and train a custom vision project, from creating necessary Azure resources to importing images, tagging them, and testing the trained model for accuracy.
8:43
This video walks through using Azure OpenAI Services within a Fabric Jupyter notebook to automatically summarize large sets of semi-structured user reviews. It also covers prompt engineering, secure secret storage in Azure Key Vault, and data handling with Delta tables in a Fabric Lakehouse environment.
10:58
This video walks through the process of setting up an Azure AI Search service with integrated vector embeddings to index documents for use in Retrieval Augmented Generation (RAG) within generative AI Q&A solutions. It then demonstrates creating a Streamlit Python application that interacts with an OpenAI large language model, utilizing the indexed data from Azure AI Search as the RAG data source.
33:16
This video walks through the process of building an end-to-end analytics reference architecture for the National Football League (NFL) using Microsoft Fabric. Viewers will learn how to download and process NFL data, create a Fabric workspace and Lakehouse, deploy Power BI semantic models, and develop AI-powered analytics agents, all while exploring key concepts like data modeling and agent evaluation workflows.