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.
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.
CSE teamed up with Conservation Metrics to use Active Learning to allow for more efficient data labeling for object detection projects.
The Commercial Software Engineering team (CSE) partnered with Axonize to automate the process of deploying apps to Kubernetes, and expose these apps to the internet via a single IP. This post is about enabling applications in your Kubernetes cluster to programmatically install helm charts and expose them through a single public facing IP.
Earlier this year, Dataiku and Microsoft joined forces to add extra flexibility to DSS on HDInsight, and also to allow Dataiku customers to attach a persistent edge node on an HDInsight cluster – something which was previously not a feature supported by the most recent edition of Azure HDInsight. Â
Developing robust algorithms for self-driving cars requires sourcing event data from over 10 billion hours of recorded driving time. CSE worked with Cognata, a startup developing simulation platforms for autonomous vehicles, to build a Jenkins pipeline and Terraform solution that enabled our partner to dynamically scale GPU resources for their simu...
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 te...
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.
