Deploy ML models to production

Productionize ML models with MLOps pipelines

Taking a trained model to production when operational monitoring and automated retraining are concerns

• · model exports
• · training data
• · monitoring thresholds

• · deployment config
• · monitoring dashboard
• · A/B test setup

• · Docker
• · Kubernetes
• · MLflow
• · Feast
• · Triton Inference Server

• · trained model ready
• · infrastructure access

• · data drift undetected
• · canary rollout triggers latency spike
• · feature store unavailable

• · SLO benchmarks
• · drift detection thresholds
• · EMV cost analysis