Developing Azure Applications typically requires interacting with many azure services. When it's not practical or permitted to use actual Azure services in development, emulators like Azurite come to the rescue.
You've probably used %pip install to include open source libraries in your Fabric notebooks, but did you know you can also easily install your own custom Python libraries?
When we create a Fabric Lakehouse, a SQL Server connection string is created automatically. This connection string can be used to query Lakehouse tables! This post walks through the code to create a C# app that reads data from a Lakehouse table.
This post is an end-to-end tutorial showing how to containerize a Python-based Streamlit data app with Docker and publish it as an Internet-facing web app using Azure Container Registry and Azure App service.
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.
When coloring series on in a Python Matplotlib plot, you can use color names. This post shows each possible name and a visual example of each corresponding color.
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.
Learn how to use Azure AI Vision to enrich image data for analysis in Power BI. This video demonstrates how to generate descriptive metadata for images using Azure Vision AI, and then incorporate it into a Power BI analysis solution.