AI Infrastructure
A model serving platform hosts multiple ML models behind a unified API, supporting canary deployments (route 5% of traffic to the new model), A/B testing for model comparison, and automatic rollback if error rates spike. This OCI-native design uses OKE for independent model deployments. The OCI Cache feature store ensures consistent feature computation between training and serving, eliminating the common train/serve skew problem.
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Each model version runs in its own OKE deployment, enabling independent scaling based on model-specific traffic. OCI Functions handles traffic splitting logic for canary and A/B testing. OCI Cache stores the feature store for low-latency feature lookups during inference. Object Storage stores model artifacts, and Functions handles model loading into OKE pods. OCI Monitoring triggers automatic rollback when error rate thresholds are exceeded.
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