Loading content
Deploying and operating ML/AI models on private infrastructure instead of managed cloud services, focusing on control, data sovereignty, latency and compliance.
Self-hosted models refers to deploying and operating AI/ML models on private infrastructure rather than managed cloud services. It emphasizes data sovereignty, low-latency inference, compliance and full control over models, resources and integrations. Operations, monitoring and model updates must be supported by organizational capabilities.
Mean and p95 latency of inference requests measured under production load.
Percentage system availability of the model-serving stack within a time period.
Share of incorrect or deviating predictions compared to validation data.
Bank operates fraud-detection models fully on-premise due to regulatory requirements.
Hospital runs image classification models locally to protect patient data.
Manufacturing uses locally deployed models for real-time anomaly detection without cloud latency.
Define requirements and compliance criteria.
Provision and segment infrastructure (network, hardware).
Build CI/CD pipeline for model tests and deployments.
Introduce monitoring, logging and alerting.
Test rollback and incident response plans.