MLOps Tutorial#1. Continuous Integration (CI/CD) for ML Pipelines with Github Actions

3 Comments



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

source

3 Comments
    • blank
      Data Science Garage
      May 16, 2022 17:44 pm Reply

      Thank you for watching this video. I am going to prepare more videos dedicated for MLOps in near future. If you want to get it first, subscribe!

      If you liked this video, probably you would be interested into this one also:
      Run Python on AWS EC2 (Step by Step Example) – https://youtu.be/qUHQuZjTOFA

    • blank
      Johann Piedras
      May 16, 2022 17:44 pm Reply

      Thank you so much for sharing this! Wow!

    • blank
      Pravin Kumar
      May 16, 2022 17:44 pm Reply

      You have no idea how much I am searching for a video like this. Thank you so much

Leave us a comment