My Workflow

Managing machine learning model is a tedious job and specially when dealing with large datasets.To automate a machine learning model like to train it,evaluate it can be done by using CML(Continuous Machine Learning) as a part of MLOps(Machine Learning Operations) which is a CI/CD tool for Machine Learning projects.

In the following workflow a tensorflow regression model is trained with CML and along with it's model performance which is also recorded and will be generated on a PUSH GitHub event.

name: mlops-tensorflow-regression
on: [push]
jobs:
  run:
    runs-on: [ubuntu-latest]
    container: docker://dvcorg/cml-py3:latest
    steps:
      - uses: actions/checkout@v2
      - name: 'Train the model'
        env:
          repo_token: ${{ secrets.GITHUB_TOKEN }}
        run: |
          # Your ML workflow goes here
          pip install -r requirements.txt
          python model.py

          echo "## Model Metrics" > report.md
          cat metrics.txt >> report.md

          echo "\n## Model Performance" >> report.md
          echo "Model performance metrics are on the plot below." >> report.md

          cml-publish model_results.png --md >> report.md

          cml-send-comment report.md

This model metrics will be sent with an email and also on pushing the code from GitHub bot.

This is what model performance looks like when you push the code and run the above workflow.

• The first image is of getting reply after pushing the code
  

• The second image is about getting results on an email
  

  Submission Category:

This workflow falls in the category of DIY Deployments

Yaml File or Link to Code

Thank You for reading this as this was my first Github Actions and I really enjoyed and learnt new while building this and a huge thank you to the Dev community and GitHub organization for organising this Github Actions hackathon.