This code story describes CSE's work with ZenCity to create a data pipeline on Azure Databricks supported by a CI/CD pipeline on TravisCI. The aim of the collaboration was to create a pipeline capable of processing a stream of social posts, analyzing them, and identifying trends.
We worked with Aveva to build 3DToolkit, a toolkit for creating powerful cloud-based 3D experiences that stream on low-powered devices with WebRTC.
The ability to correctly identify entities, such as places, people, and organizations, adds a powerful level of natural language understanding to applications. This post introduces a MIT-licensed one-click deployment to Azure for web services that lets developers get started with a wide range of natural language tasks in 5 minutes or less, by consuming simple HTTP services for language identification, tokenization, part-of-speech-tagging and named entity recognition.
We created an easy-to-use tool for visualizing data from Microsoft Azure Application Insights with a dashboard framework. Our solution can be applied to different bot scenarios as well as other scenarios involving the service.
We collaborated on an image classification pipeline to perform automatic face detection and matching using machine learning via Microsoft Cognitive Services Face API. Our project was built with Azure Functions to process images using message queues.