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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.

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.

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.

Microsoft and Land O'Lakes partnered to develop an automated solution to identify sustainable farming practices given thousands of satellite images of Iowan farms. Our primary goal was to reduce the reliance on manual interviewing of farmers and make it more profitable for farmers to follow sustainable farming practices. To tackle this issue our team deployed a highly scalable Batch AI cluster on Azure and then performed distributed deep learning model training with Horovod.

In this article, we introduce a technique to rapidly pre-label training data for image segmentation models such that annotators no longer have to painstakingly hand-annotate every pixel of interest in an image. The approach is implemented in Python and OpenCV and extensible to any image segmentation task that aims to identify a subset of visually distinct pixels in an image.

In early 2018, Microsoft partnered with a successful international online fashion retailer to develop an efficient system for logging newly arrived apparel items and quickly determining whether the item is already in stock

We explored possible techniques to identify duplicate catalogue entries in fashion retail through image similarity with deep learning and computer vision.

Creating an object detection model using Microsoft's open source deep learning framework CNTK and its implementation of Fast-RCNN.

Building a video tagging tool on top of CNTK to enable developers to create, review and iterate object detection models.

Findings from a pilot project where we identified reusable patterns, and ways to avoid common pitfalls when implementing object detection and tracking in Windows apps.