Showing results for Azure - ISE Developer Blog

May 7, 2019
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Preventing Rhino Poaching With Microsoft Azure

Olga Liakhovich
Olga Liakhovich

In the last eight years, poaching in Africa has been happening at an alarming rate. Currently, the continent loses three rhinos a day. As part of our team’s mission to find new ways that technology can positively impact our world, we’ve been collaborating with the Combating Wildlife Crime team Peace Parks Foundation (PPF), in partnership with

Machine LearningContainers
Dec 12, 2018
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Social Stream Pipeline on Databricks with auto-scaling and CI/CD using Travis

Mor Shemesh
Mor Shemesh

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.

DevOpsBig DataAzure App Services
Jun 1, 2018
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Building a Private Ethereum Consortium

Jason Poon
Jason Poon

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 t...

ContainersBlockchain
Feb 15, 2018
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Copy Custom VM Images on Azure

Tamir Kamara
Tamir Kamara

We created an azure-cli extension to simplify the process of distributing VM images globally.

DevOps
Nov 21, 2017
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Autoscaling Deep Learning Training with Kubernetes

Rita Zhang
Rita Zhang

We explore how we worked with a customer to add autoscaling capability to a Kubernetes cluster to meet bursty demands for deep learning training in a cost-efficient manner.

Machine LearningContainers
Jul 1, 2017
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Building an Image Classification Pipeline Using Serverless Architecture

Jason Fox
Jason Fox

We collaborated on an image classification pipeline to perform automatic face detection and matching using machine learning via Microsoft Cognitive Services Face API. Our project was built with Azure Functions to process images using message queues.

Machine LearningAzure App Services