Nestlé Skin Health partnered with Microsoft to develop a deep learning model powered mobile app able to assess acne severity using only uploaded selfie images as a source.
Deep learning algorithms capable of learning and predicting customer behavior are allowing businesses to intervene with the right retention offers at the right time. CSE recently partnered with Majid Al Futtaim Ventures (MAF) to design and deploy a machine learning solution to predict attrition.
This code story describes a collaboration with ZenCity around detecting trending topics at scale. We discuss the datasets, data preparation, models used and the deployment story for this scenario.
Over the course of twelve months Microsoft and BMW partnered three different times to help BMW with its vision for technical transformation. An open-source package called ROS-Industrial was used to help provide the building blocks for the robotics work.
CSE teamed up with Conservation Metrics to use Active Learning to allow for more efficient data labeling for object detection projects.
A scalable unsupervised approach for driver safety estimation on Pointer Telocation's dataset
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.
Can Machine Learning help with detecting sustainable farming practices? In this blog post inspired by our collaboration with Land O'Lakes we share the lessons we learned in the image segmentation space.
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.
