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Creating an end to end machine learning pipeline using Docker, Heroku, Better Code, PyTest, and more such tools.

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A way to ship code in small increments and iterations is by using a Continuous Integration and Continuous Deployment, or CI/CD, pipeline. In this tutorial we’ll go through all the steps in setting up such a pipeline using free and hosted services. From start to end this tutorial shows you in 9 steps how to:

  • Write a little Python program (not Hello World)
  • Add some automated tests for the program
  • Push your code to GitHub
  • Setup Travis CI to continuously run your automated tests
  • Setup Better Code Hub to continuously check your code quality
  • Turn the Python program into a web app
  • Create a Docker image for the web app
  • Push the Docker image to Docker Hub
  • Deploy the Docker image to Heroku

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Creating an end to end machine learning pipeline using Docker, Heroku, Better Code, PyTest, and more such tools.

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