Using GitHub Actions as a cheap (and free) MLOps tool alternative

f1-mlops.yml

# Pre-process and model building
name: F1 Prediction MLOps

on:
  workflow_dispatch

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - name: Checkout latest from source control
      uses: actions/checkout@v2
    
    - name: Set up Python 3.8
      uses: actions/setup-python@v2
      with:
        python-version: "3.8"
        
    - name: Set up Node.js environment
      uses: actions/setup-node@v2
      with:
          node-version: '14'
          
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
        
    - name: Install Chrome
      uses: browser-actions/setup-chrome@latest
      
    - name: Python scripts => Ergast to MongoDB (data sourcing)
      env:
        MONGO_DB_USER: ${{secrets.MONGO_DB_USER}}
        MONGO_DB_PW: ${{secrets.MONGO_DB_PW}}
      run: |
        python './scripts/1-ergast-to-mongo.py'
        
    - name: Python scripts => Creating csv results from MongoDB (data preparation)
      env:
        MONGO_DB_USER: ${{secrets.MONGO_DB_USER}}
        MONGO_DB_PW: ${{secrets.MONGO_DB_PW}}
      run: |
        python './scripts/2-create-csv-from-mongo.py'
        
    - name: Python scripts => Feature Engineering
      run: |
        python './scripts/3-feature-engineering.py'
        
    - name: Python scripts => Machine Learning Model Building and Scoring
      run: |
        python './scripts/4-build-ml-model.py'
        
    - name: Install Serverless Framework
      run: npm install -g serverless
      working-directory: ./flask-api    
      
    - name: Serverless AWS authentication
      run: sls config credentials --provider aws --key ${{ secrets.AWS_KEY }} --secret ${{ secrets.AWS_SECRET }}
      working-directory: ./flask-api
      
    - name: Serverless WSGI
      run: sls plugin install -n serverless-wsgi
      working-directory: ./flask-api
      
    - name: Deploy ML model and API
      run: npm run deploy
      working-directory: ./flask-api
      env:
        AWS_ACCESS_KEY_ID: ${{secrets.AWS_KEY}}
        AWS_SECRET_ACCESS_KEY: ${{secrets.AWS_SECRET}}