The MLOps Lifecycle Explained¶
Introduction¶
The MLOps lifecycle is a loop. Production behavior feeds back into data validation, retraining, evaluation, and redeployment.
Why This Matters¶
ML systems age because data changes. A reliable lifecycle makes retraining intentional instead of an emergency response after users notice bad predictions.
Core Concepts¶
The loop includes data collection, preprocessing, training, evaluation, packaging, deployment, monitoring, and retraining. Each stage should produce evidence such as reports, metrics, artifacts, or deployment revisions.
Practical Example¶
A practical lifecycle can start as make targets or CI jobs:
make validate-data
make train
make evaluate
make package
make deploy-staging
make smoke-test
validate-data: passed
evaluate: f1=0.842 threshold=0.62
package: dist/churn-model-2026-05-30.tar.gz
How This Fits in a Production Workflow¶
As the system matures, the same stages can move to Airflow, Kubeflow, Argo Workflows, GitHub Actions, or a platform pipeline.
Common Mistakes¶
- Skipping data validation because training completed.
- Promoting a model based on one metric only.
- Retraining manually with unrecorded parameters.
- Deploying without a smoke test.
Quick Checklist¶
- Is every stage automated?
- Does each stage produce an artifact or report?
- Can a failed model be traced to data, code, and parameters?
- Is monitoring connected to retraining decisions?
Related Guides¶
- What Is MLOps? A Practical Guide for Beginners
- CI/CD Pipeline for Machine Learning Projects
- Model Monitoring in Production
Summary¶
Understand the MLOps lifecycle as a loop from data collection through deployment, monitoring, and retraining.