12 videos
9:35
This video walks through the process of calling Azure AI Document Intelligence services using the REST API, demonstrating how to extract data from images and PDF scans of forms for integration into data solutions.
5:42
This video walks through integrating Azure AI Translation into a Fabric Data Lake solution using Synapse ML features within Spark notebooks. Viewers will learn how to apply Language AI Services in 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 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 and deploying pre-trained large language models from Azure ML's model catalog, then demonstrates how to access these models via a REST API. Viewers will learn to create a resource group, set up an Azure workspace, and deploy a model endpoint for use in their applications.
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. You will see a live demonstration of how to set up and configure the necessary components to enrich image data for more insightful analytics.
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 set up, test, and run these models locally on iOS devices, enhancing application functionality whether connected to the cloud or operating independently.
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 learn how to set up and train a custom vision project, import images, tag them for training, and test the model's accuracy in detecting packaging issues.
8:43
This video walks through using Azure OpenAI Services within a Fabric Jupyter notebook to automatically summarize large collections of semi-structured user review data, covering prompt engineering and secure secret storage in Azure Key Vault. It also demonstrates how to read from and write to Delta tables in a Fabric Lakehouse.
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 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 NFL data using Microsoft Fabric. Viewers will learn how to download and process NFLVerse data, create a Fabric workspace and Lakehouse, deploy Power BI semantic models, and develop Fabric Data Agents for AI-powered analytics.