MLOps Standards
MLOps Standards
How machine learning solutions are built, deployed and operated — with reproducibility, automation, governance, observability, security and scalability.
Core principles
The principles we apply
ReproducibilityAutomationGovernanceObservabilitySecurityScalability
Lifecycle
How it flows
Data→Features→Training→Validation→Deployment→Monitoring→Improvement
Standards
What the standard covers
Feature Engineering
- Feature Store
- Feature Versioning
- Feature Lineage
Model Training
- Training Pipelines
- Experiment Tracking
- Model Registry
- Model Versioning
Deployment
- Batch Inference
- Real-Time Inference
- Edge Inference
- Canary Releases
- Blue-Green Releases
Monitoring
- Accuracy
- Latency
- Throughput
- Drift
- Cost
Validation
- Model Testing
- Bias Testing
- Robustness Testing
- Safety Testing
- Performance Testing
Evidence produced
Artefacts & evidence
Every standard produces reviewable, audit-ready evidence.
MLOps Architecture
Model Registry
Monitoring Framework
Model Governance Framework
Production AI Platform