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-This repository contains samples showing how to build an AI application with DevOps in mind. For an AI application, there are always two streams of work, Data Scientists building machine learning models and App developers building the application and exposing it to end users to consume.
+This repository contains samples showing how to build an AI application with DevOps in mind. For an AI application, there are always two streams of work, Data Scientists building machine learning models and App developers building the application and exposing it to end users to consume. test
 
 In this tutorial we demonstrate how you can build a continous integration pipeline for an AI application. The pipeline kicks off for each new commit, run the test suite, if the test passes takes the latest build, packages it in a Docker container. The container is then deployed using Azure container service (ACS) and images are securely stored in Azure container registry (ACR). ACS is running Kubernetes for managing container cluster but you can choose Docker Swarm or Mesos.