Why MLOps architecture matters
A model is not useful until it is deployed, monitored, versioned, and maintained. MLOps provides that production lifecycle.
Core components
A practical MLOps setup includes several connected systems.
- 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 needs.
Monitoring AI systems
Traditional monitoring is not enough. AI systems also need model quality checks, drift detection, latency tracking, and cost monitoring.
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FAQ
What is MLOps architecture?
MLOps architecture connects data pipelines, model training, model registry, deployment, monitoring, and retraining workflows.
Is MLOps only for large companies?
No. Startups deploying production AI models also need basic MLOps discipline.