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.
We demonstrate how to train Object Detection models using CNTK and Tensoflow DNN frameworks. Azure ML Workbench is used as the main training and model hosting infrastructure.
We demonstrate how we migrated the compute resources of a genomics intelligence platform to Azure, transferring more than 100 TB of blob storage and handling application secrets without embedding the Azure SDK.
Situm, a company that offers high precision indoor navigation, looked to Kubernetes on Azure to provide high availability and scalability for their services. As of Kubernetes v1.6.5, Kubernetes on Azure supports both UDP and TCP workloads, and respects the Kubernetes Service spec's sessionAffinity.
We will cover the steps necessary to get Deis Workflow up and running on Azure, and address the production deployment steps in depth.
We partnered with General Fusion to develop and deploy their new Pachyderm-based data infrastructure to Microsoft Azure. This post walks through General Fusion’s new data architecture and how we deployed it to Azure.
