Why ML Needs Specialized CI/CD

You've built a working model. You've even deployed it once. But six months later, it's drifting, the training pipeline is broken, and nobody knows which version is in production. Welcome to the reality of ML systems without proper CI/CD.

The Three Dimensions of ML Change

Traditional CI/CD handles one source of change: code. ML systems have three:

Traditional Software:
  Code → Build → Test → Deploy

ML Systems:
  Code    ─┐
  Data    ─┼─→ Build → Test → Validate → Deploy → Monitor
  Model   ─┘

Each dimension creates unique challenges:

Dimension	Change Type	Challenge
Code	Feature engineering, pipeline logic	Standard CI/CD applies
Data	Schema changes, distribution drift	Need data validation gates
Model	Retraining, hyperparameter tuning	Need model validation gates

Why Traditional CI/CD Falls Short

Traditional CI/CD	ML CI/CD Need
Test code correctness	Test data quality + code + model
Binary pass/fail tests	Threshold-based validation (accuracy > 0.85)
Deterministic builds	Non-deterministic training
Fast builds (minutes)	Long training jobs (hours/days)
Small artifacts (MB)	Large artifacts (GB models, TB datasets)
Version code only	Version code + data + models + experiments

Real-World ML CI/CD Failures

These scenarios happen without proper ML CI/CD:

Scenario 1: Silent Model Degradation

Week 1: Model accuracy 94%
Week 8: Model accuracy 71% (nobody noticed)
Week 12: Customer complaints spike

Fix: Automated monitoring in CI/CD that catches drift.

Scenario 2: Training-Serving Skew

# Training pipeline
features['age'] = normalize(data['age'])  # Uses training set stats

# Serving pipeline
features['age'] = normalize(data['age'])  # Uses... what stats?

Fix: Data validation that catches feature inconsistencies.

Scenario 3: Unreproducible Models

Developer: "I trained this model last month"
Team: "Which data version? Which code commit? Which hyperparameters?"
Developer: "...I didn't track that"

Fix: Versioning in CI/CD that tracks all inputs.

The ML CI/CD Triad

Effective ML CI/CD must handle:

┌────────────────────────────────────────────────────────┐
│                    ML CI/CD Triad                      │
├────────────────────────────────────────────────────────┤
│                                                        │
│    ┌─────────┐    ┌─────────┐    ┌─────────┐         │
│    │  Code   │    │  Data   │    │  Model  │         │
│    │ Testing │    │Validation│    │Validation│        │
│    └────┬────┘    └────┬────┘    └────┬────┘         │
│         │              │              │               │
│         └──────────────┼──────────────┘               │
│                        ▼                              │
│              ┌─────────────────┐                      │
│              │  Deployment     │                      │
│              │  Gate           │                      │
│              └─────────────────┘                      │
└────────────────────────────────────────────────────────┘

Key ML CI/CD Capabilities

Capability	Purpose	Tools
Data versioning	Track dataset changes	DVC, LakeFS
Data validation	Ensure data quality	Great Expectations, Pandera
Experiment tracking	Log runs and metrics	MLflow, W&B
Model validation	Check model quality	Custom tests, threshold gates
Artifact management	Store/version large files	DVC, S3, GCS
Pipeline orchestration	Coordinate training/deployment	GitHub Actions, GitLab CI

Market Reality

Organizations recognize the need:

• 73% of ML teams cite data quality as their biggest challenge
• 90% of engineering teams now use AI in workflows
• Only 22% have mature CI/CD for ML systems

Key Insight: ML CI/CD isn't optional—it's the difference between a working demo and a reliable production system.

Next, we'll explore the anatomy of an ML CI/CD pipeline. :::