From 92103f16f4b61c2ace82a22e8262015d8381a555 Mon Sep 17 00:00:00 2001 From: Richin Jain Date: Mon, 6 Nov 2017 16:37:39 -0500 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 02fbd32..82605da 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -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.