MLOps Tutorial#1. Continuous Integration (CI/CD) for ML Pipelines with Github Actions
Continuous Integration is when you make your Machine Learning (ML) pipelines run automatically when Push your Git commits and get quick results about your ML model in ReadMe file available to all team members in your Github workspace and in your email in user friendly format straightforward.
Let’s imagine, you build a Docker file every time when you make changes in your ML model Python file, then send the results with plots and related information directly from the Python script. Github actions can do it and save a lot of time while improving your model. This is a simple CI-CD Pipeline.
The primary idea comes from DevOps and is simple. Every time you push your commits to your Github branch, a new Docker container with dependencies is being created. Then model training is starting-up, and finally delivering your results. All the workstream processes are defined in a special Continuous Machine Learning (CML) YAML file.
In this example, I am using a simple Tensorflow Regression model, a simple graph made by matplotlib Python library and a standard Github account with a public repository.
For this purpose we will be using:
– GitHub Actions: https://github.com/features/actions
– CML: https://github.com/iterative/cml
The example project is available here on Github: https://github.com/vb100/ml-ops-ci/tree/experiment
The content of the tutorial:
0:00 – Intro
1:15 – What the model is about.
1:30 – Create a new Github branch with cml.yaml file for ML workstream
2:46 – Run Git Action with print statement in terminal
8:13 – Run Git Action with text information in Readme file
13:18 – Run Git Action with graphical information in Readme file
19:54 – Improve the model and run GitAction again.
23:09 – Summary and Thank you
This is the first video in a series dedicated to MLOps (Machine Learning Operations). Subscribe channel to get more!
#mlops #gitactions #docker
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