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.
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.
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 simulations.
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.
Over the past two years, Microsoft and Webjet have collaborated to build a blockchain-based solution, Rezchain, to help travel companies reduce payment disputes. In this code story, we’ll share the lessons learned in creating the Rezchain consortium. In particular, we'll focus on how we solved the challenges involved with enabling Ethereum nodes to peer across virtual networks.