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Copy file name to clipboardexpand all lines: README.md
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# Python Flask ML Demo Project with CI/CD in AWS EC2
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# Python Flask ML Demo Project with CI/CD
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| 1. CI Status <br>(build and test) | 2. CD status <br>(docker build and push in DockerHub) | 3. CD Status <br>(AWS EC2 docker pull and run) |
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| ---- | ---- | ---- |
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[Link to YML: docker-image.yml](https://github.com/matiaspakua/ml-demo-project/blob/189d424ad72ed630ede8a489aa8804dd2a153403/.github/workflows/docker-image.yml)
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### Step 03
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A third workflow is executed when the first two are executed successfully and the image is pulled and deployed in a AWS Ubuntu EC2 instance.
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A third workflow is executed when the first two are executed successfully and the image is pulled and deployed in a AWS Ubuntu EC2 instance or AZURE Container Instance:
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[Link to YML: docker-run.yml AWS EC2](https://github.com/matiaspakua/ml-demo-project/blob/189d424ad72ed630ede8a489aa8804dd2a153403/.github/workflows/docker-run.yml)
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or
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[Link to YML: docker-run.yml AZURE](https://github.com/matiaspakua/ml-demo-project/blob/e6cf710bdd2e8c2b8ab3675e4599f919735db657/.github/workflows/deploy-azure.yml)
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[Link to YML: docker-run.yml](https://github.com/matiaspakua/ml-demo-project/blob/189d424ad72ed630ede8a489aa8804dd2a153403/.github/workflows/docker-run.yml)
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### Step 05
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The last step is wait until the image is pulled and running in the EC2 Ubuntu instance and access the public URL to log into the landing page of the app:
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The last step is wait until the image is pulled and running in the EC2 Ubuntu or in AZURE Container instance and access the public URL to log into the landing page of the app:
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(Depending of the Free Tier that I have at the moment I use one or another)
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* AWS EC2: [Landing page on EC2 instance](http://ec2-54-172-218-192.compute-1.amazonaws.com:8080/)
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* Azure Container: [Landing page on AZURE instance](https://ml-demo-project-app.azurewebsites.net/)
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[Landing page on EC2 instance](http://ec2-54-172-218-192.compute-1.amazonaws.com:8080/)
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