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 & AI Strategy Consultant

Over 13 years of experience implementing AI solutions across Global Fortune 500 companies and startups. Specializes in enterprise-scale AI transformation, MLOps architecture, and AI governance frameworks.

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 →