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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…
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Model orchestration coordinates multiple model-related steps and services, such as data preparation, selection, inference, post-processing, and handoff. It describes the flow and dependencies, not the individual model.
It grew from workflow and pipeline automation in MLOps, when separate training and serving steps needed to become repeatable, observable flows. Projects such as Kubeflow and KServe represent this Kubernetes-oriented evolution.
Picture a conductor for a pipeline: it starts a step only when prerequisites are ready, assigns work to specialized components, handles results, and manages failures or retries.
It defines the ordered sequence of tasks performed by a model process.
It determines when a step may start and which outputs from earlier steps it needs.
It handles scheduling, retries, parallelism, and failure behavior across the flow.
Orchestration matters when model processes contain several repeatable steps that must run reliably, at scale, or on a schedule.
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