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Model APIs expose ML models via standardized interfaces and simplify integration, versioning and scaling of inference services.
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, monitoring and autoscaling. Best practices address latency optimization, resource management and secure data handling.
95th percentile of response times for inference requests; important for UX and SLAs.
Requests per second the system can handle stably.
Share of failed API calls or erroneous predictions.
Product pages call a model API for personalized recommendations in real time.
Payment transactions are synchronously validated against a scoring API.
Conversational model is served via a gRPC endpoint for multiple channels.
Package model artifact and document metadata (input/output schema).
Define and validate API contract (OpenAPI/Protobuf).
Create serving container, run latency and accuracy tests.
Set up deployment pipeline (CI/CD) and configure canary rollout.
Enable observability, alerting and autoscaling.