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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.

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.

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 quality of...

We use IoT sensors to collect positional and motion data from professional and amateur skiers to classify expertise and skill level through machine learning.

Using Microsoft Cognitive Services Vision API Optical Character Recognition within Azure ML Studio to Predict Expense Type from Receipts.

At Microsoft, we are working with a number of car manufacturers on processing telemetry streams. One of the key business scenarios for the incoming real-time car telemetry is to offer real-time driver risk scoring for end user driving guidance applications, fleet administration, and usage-based insurance scenarios.

In this post, you will learn how to implement a MapReduce job to efficiently extract XML data from HDFS sequence files using a custom RecordReader.