Movies and TV shows require multiple takes per scene and may have a substantial amount of archival footage as a result. Here, we use Azure Cognitive Services and custom code to develop a multi-model Machine Learning (ML) solution to automatically detect discardable footage to save media companies manual archiving hours and storage space.
Media companies capture footage filmed for the entire day in what's known as ‘digital dailies’. When talking about terabytes and petabytes of content, storage costs can be a factor. Lets explore Machine Learning approaches to identify which content can be archived or discarded which will save on those storage costs.
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.
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.
This code story describes how we created a skeletal framework to achieve the following:
We found a few challenges in addressing these goals. For instance, the ...
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.