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Coordination and control of the lifecycle and production deployment of machine learning models across platforms.
Model orchestration coordinates model lifecycle, deployment and request routing of ML models in production. It combines model versioning, serving, A/B testing and monitoring into repeatable workflows. The goal is high availability, consistent inference and automated rollouts across platforms. Implementations require integration with feature stores, CI/CD and observability stacks plus governance and security policies.
95th percentile of response times for model endpoints.
Share of successfully promoted models per time period.
Share of failed or rejected inference requests.
Pipeline that orchestrates training, packaging and deployment.
Using KServe for scalable serving and model versioning.
Registry-based promotion of models from staging to production.
Define model registry and versioning rules; connect to CI/CD.
Set up serving infrastructure and routing rules.
Implement observability, tests and rollback mechanisms.
Train operations teams and establish governance policies.