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Fixed typo #16

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Fixed typo #16

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -2,7 +2,7 @@ This repository contains samples showing how to build an AI application with Dev

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.

The application securely pulls the latest model from an Azure Storage account and packages that as part of the application. Teh deployed application has the app code and ML model packaged as single container.
The application securely pulls the latest model from an Azure Storage account and packages that as part of the application. The deployed application has the app code and ML model packaged as single container.

This decouples the app developers and data scientists, to make sure that their production app is always running the latest code with latest ML model.

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