MLOps CI/CD Pipeline

MLOps

Create automated ML pipelines for continuous integration and deployment

90 mins

Overview

  • •Understanding MLOps fundamentals and CI/CD for ML systems
  • •Setting up version control for ML projects and data
  • •Implementing automated testing for ML models
  • •Building continuous training and deployment pipelines
  • •Monitoring ML systems in production
  • •Infrastructure as Code (IaC) for ML environments

Implementation Scenarios

Version Control for ML Assets

DevOps Foundation

Setting up version control for code, data, and ML artifacts

Implementation Steps

  • Structuring ML projects for version control
  • Setting up Git workflows for ML teams
  • Data versioning with DVC and Git-LFS
  • ML experiment tracking and versioning
  • Model artifact versioning
  • CI/CD pipeline integration with Git actions

Code Example

# Example code for setting up Git and DVC for an ML project
# 1. Initialize Git repository
git init
git add README.md
git commit -m "Initial commit"

# 2. Set up DVC for data versioning
pip install dvc
dvc init
dvc config core.autostage true

# 3. Add data sources to DVC tracking
mkdir -p data/raw data/processed
dvc add data/raw/training_data.csv
git add data/.gitignore data/raw/training_data.csv.dvc
git commit -m "Add raw training data"

# 4. Set up remote storage for data
dvc remote add -d storage s3://my-ml-bucket/dvcstore
dvc push

# 5. Create simple CI workflow for GitHub Actions
cat > .github/workflows/ml-pipeline.yml << EOF
name: ML Pipeline

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.10'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run tests
      run: |
        pytest tests/
EOF

git add .github/workflows/ml-pipeline.yml
git commit -m "Add CI workflow"

Tools & Libraries

GitDVCGitHub ActionsGit-LFSMLflow

Instructor

Nim Hewage

Nim Hewage

Co-founder

MLOps and DevOps specialist with extensive experience building production machine learning systems. Focuses on creating reliable, scalable, and automated ML pipelines for enterprise applications.

Related Resources

Tutorial Materials

Additional Learning Resources

MLOps for Production

Best practices for implementing MLOps in production environments

View documentation →

Kubeflow Documentation

Documentation for Kubeflow ML pipeline platform

Explore resources →

MLflow Documentation

Open-source platform for managing the ML lifecycle

View docs →