MLOps Architecture Guide for Production AI Systems
A practical MLOps architecture guide covering model deployment, CI/CD, feature pipelines, model registry, monitoring, drift detection, and retraining workflows.
Why MLOps architecture matters
A model is not useful until it is deployed, monitored, versioned, and maintained. MLOps provides the production lifecycle required to operate AI systems safely.
Core components
A practical MLOps setup includes several connected systems that support the full model lifecycle.
- ✓Data pipeline
- ✓Feature processing
- ✓Experiment tracking
- ✓Model registry
- ✓CI/CD for models
- ✓Inference service
- ✓Monitoring
- ✓Drift detection
- ✓Retraining workflow
Deployment patterns
Models can be deployed as APIs, batch jobs, embedded services, or event-driven inference workflows depending on product requirements, latency expectations, and cost constraints.
Monitoring AI systems
Traditional monitoring is not enough for AI systems. Production models also need model quality checks, drift detection, latency tracking, error monitoring, and cost visibility.
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Frequently Asked Questions
What is MLOps architecture?
MLOps architecture connects data pipelines, feature processing, model training, model registry, deployment, monitoring, drift detection, and retraining workflows.
Is MLOps only for large companies?
No. Startups deploying production AI models also need basic MLOps discipline for reliability, monitoring, versioning, and safe model updates.