LoopyLuci MLOps CI/CD Pipelines
Building CI/CD pipelines for machine learning systems — from data validation through model training, evaluation, deployment, and monitoring automation.
When to Use
- Automating ML model training and deployment
- Building CI/CD for ML systems (not just application code)
- Ensuring reproducibility across ML pipeline runs
- Implementing staging → production model promotion
- Automating model retraining on schedule or trigger
MLOps CI/CD Pipeline
MLOPS_PIPELINE_STAGES = {
'data_validation': 'Great Expectations checks, schema validation, data drift detection',
'training': 'Triggered by: schedule (weekly), new data, code change, or manual',
'evaluation': 'Compare against champion model, statistical tests, holdout set',
'staging': 'Deploy to staging, run integration tests, shadow traffic',
'production': 'Promote to production, monitor drift and performance',
'monitoring': 'Continuous data and concept drift detection, performance alerts',
}
class MLPipeline:
"""Define an MLOps CI/CD pipeline."""
def __init__(self, name: str, repo: str):
self.name = name
self.stages = []
def add_stage(self, name: str, script: str,
requirements: List[str] = None):
self.stages.append({
'name': name, 'script': script, 'reqs': requirements or [],
'dependencies': self.stages[-1] if self.stages else None,
})
def to_ci_config(self, platform: str = 'github') -> str:
if platform == 'github':
return f"""name: MLOps - {self.name}
on: [push, workflow_dispatch, schedule(cron: '0 6 * * 0')]
jobs:
train:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Train Model
run: python train.py
- name: Evaluate
run: python evaluate.py
- name: Deploy to Staging
run: python deploy.py --stage staging"""
Verification Checklist
- Pipeline defined with stages (data validation, train, eval, deploy, monitor)
- Triggers configured (code push, schedule, data arrival)
- Model evaluation gates before production deployment
- Automated rollback on performance degradation
- Experiment tracking integrated with each pipeline run
- Model registry updated after successful deployment
- Monitoring and alerting for pipeline failures