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Model APIs expose machine learning models or decision services via standardized interfaces. They enable low-latency inference, versioning and easy integration into applications as well as observability and scaling. Typical use cases include real-time scoring, batch predictions and A/B rollouts. Implementations cover REST/gRPC endpoints, authentication, monit…
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What organizes, connects, or makes decisions possible.
Model APIs are programmable interfaces through which applications call a trained model, submit inputs, and receive results. They hide execution details behind a stable contract.
They combine the older idea of an application programming interface with operating machine-learning models. Standard descriptions such as OpenAPI made input and output formats, errors, and access paths explicit; inference servers such as Triton implement these services in practice.
A model API is like a service counter: a client submits a validated request, the service selects or loads a model, runs inference, and returns a structured response. The contract separates callers from model hardware and implementation.
It defines the inputs, outputs, errors, and authentication rules of a call.
It carries the data and optional parameters needed for a prediction.
It handles routing, model invocation, and response formation independently of the client.
Model APIs make models usable in products while allowing clients, models, and infrastructure to evolve and scale independently.
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