CI/CD for Machine Learning Pipelines

Fundamentals

Pipelines

• Build → test → package → deploy
• Data and model validations

Environments

• Dev → Staging → Production
• Feature flags and progressive delivery

How the Algorithm Works

Loop

1. Build and unit/integration tests
2. Offline validation and bias checks
3. Pre-prod shadow tests
4. Canary/blue-green deploy
5. Monitor and rollback if needed

Best Practices

Checklist

• Version everything; track lineage
• Automate tests and validations
• Progressive delivery with guardrails
• Observability and alerts at every stage
• Approvals and audit logs

Anti-Patterns

• Notebook-only releases
• No rollback plan
• Leaking test sets
• No tracking or governance
• Ignoring fairness and compliance

Worked Examples

Build → Test → Package

# CI pipeline steps with tests and validations

Deploy → Monitor → Rollback

# Canary rollout with guardrails and rollback

Implementation

Pipelines

• Build/test/package/deploy stages
• Data/model checks and gates

Infra

• Kubernetes, serverless, and batch runners
• Artifact stores and registries

Security

• SBOMs, signing, and scanning
• Secrets management

The Math Behind It

Metrics

• Change failure rate, MTTR, MTTD
• Latency/accuracy SLOs and error budgets

Risk

Model rollout risk blends statistical uncertainty and system risk; use progressive delivery to control exposure.