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This cluster consolidates concepts, platforms, and practices around AI models and their operational environments.
This segment covers deployment of AI platforms and models. Included are deployment scope, deployment artifacts, infrastructure components, runtime and scaling aspects, monitoring and failure behaviour, and integration boundaries, as well as boundaries to training and experimentation environments. Excluded are data collection, model training and governance policy definitions.
Model APIs expose ML models via standardized interfaces and simplify integration, versioning and scaling of inference services.
Deploying and operating ML/AI models on private infrastructure instead of managed cloud services, focusing on control, data sovereignty, latency and compliance.