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.
Golf performance tracking startup Arccos joined forces with Commercial Software Engineering (CSE) developers in hopes of unveiling new improvements to their “virtual caddie” this summer.
Extracting general concepts, rather than specific phrases, from documents and contracts is challenging. It’s even more complicated when applied to scanned documents containing handwritten annotations. We describe using object detection and OCR with Azure ML Package for Computer Vision and Cognitive Services API.
When it comes to identifying and understanding influential voices in social media, context is key. See how we used topic detection, sentiment analysis, and Azure Functions to automate context-aware social media insights.
This story covers how to get started with transfer-learning and build image classification models in Python with the Custom Vision Service. We compare the results with the popular Tensorflow-based models Inception and MobileNet.
This post describes in detail the Azure Cognitive Services speech-to-text WebSocket protocol and shows how to implement the protocol in Java. This enables us to transcribe audio to text in near real-time. We then show how to feed the transcribed radio into a pipeline based on Spark Streaming for further analysis, augmentation, and aggregation. The Java client is reusable across a wide range of text-to-speech scenarios that require time-efficient speech-to-text transcription in more than 10 languages including English, French, Spanish, German and Chinese.
With our solution, users can publish bot dialogs from any backend directly to MIcrosoft Bot Framework channels, with custom Navigator logic if needed.
We address the challenge of creating a custom search experience for a specific domain area. We also provide a guide for creating your own custom search experience by leveraging Azure Search and Cognitive Services and sharing custom code for iterative testing, measurement and indexer redeployment.
Claiming expenses is usually a manual process. This project aims to improve the efficiency of receipt processing by looking into ways to automate this process. << You need to include a little more context here. I understand that you don’t want to name the partner,