MLOps and Model Registry

What is MLOps? MLOps (Machine Learning Operations) applies DevOps practices to ML systems: versioning, testing, deployment, and monitoring of ML models in production.

MLOps Lifecycle

[Data] → [Training] → [Validation] → [Registry] → [Deploy] → [Monitor]
   ↑                                                              ↓
   └──────────────────── Retrain ────────────────────────────────┘

Model Registry

Core Features

Popular Tools

Model Deployment Patterns

Blue-Green Deployment

• Maintain two identical production environments
• Switch traffic between them
• Easy rollback

Canary Deployment

[100% → Old Model]
    ↓
[95% Old, 5% New] → Monitor
    ↓
[50% Old, 50% New] → Monitor
    ↓
[100% → New Model]

Shadow Deployment

• New model receives traffic but responses not used
• Compare outputs to current production
• Validate before real deployment

Rollback Strategies 1. Instant rollback: Point to previous model version 2. Gradual rollback: Shift traffic back incrementally 3. Automatic rollback: Trigger on metric thresholds

CI/CD for ML

**Example: GitHub Actions ML Pipeline**
on: [push]
jobs:
  train:
    steps:
      - run: python train.py
      - run: mlflow register-model
  validate:
    steps:
      - run: python validate.py
  deploy:
    if: validation passes
    steps:
      - run: ./deploy_to_production.sh

Best Practices

• Version everything: code, data, models, configs
• Automate testing: data validation, model quality
• Monitor in production: data drift, model degradation
• Document: model cards, data sheets, runbooks