ISE Developer Blog
Solving global tech challenges, sharing insights, and empowering developers
Latest posts
Active Learning for Object Detection in Partnership with Conservation Metrics
CSE teamed up with Conservation Metrics to use Active Learning to allow for more efficient data labeling for object detection projects.
How to Build A K8S Http API For Helm, and Serve Micro-services Using A Single IP
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.
Attaching and Detaching an Edge Node From a HDInsight Spark Cluster when running Dataiku Data Science Studio (DSS)
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
Infrastructure as Code – On demand GPU clusters with Terraform & Jenkins
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.
Unsupervised driver safety estimation at scale, a collaboration with Pointer Telocation
A scalable unsupervised approach for driver safety estimation on Pointer Telocation's dataset
Semantic Segmentation of Small Data using Keras on an Azure Deep Learning Virtual Machine
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.
Deploying a Batch AI Cluster for Distributed Deep Learning Model Training
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.
Satellite Images Segmentation and Sustainable Farming
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.
Building a Private Ethereum Consortium
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.