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.
RAG AI Knowledge Base
OpenAI Pattern
Retrieval-Augmented Generation pipeline with vector search, embedding generation, and LLM orchestration for enterprise AI apps.
Vector Database System
AI Infrastructure
Purpose-built vector database on OCI with HNSW indexing, hybrid search, metadata filtering, and multi-tenant isolation using OKE and Autonomous Database.
Batch Inference Pipeline
AI Infrastructure
Large-scale batch prediction pipeline on OCI for processing millions of records through ML models with Data Science and OKE.
Content Moderation AI Pipeline
AI Infrastructure
Automated content moderation on Azure with Azure AI Content Safety, multi-modal analysis, human review queues, and policy enforcement.
Multi-Agent AI System
AI Infrastructure
Orchestrated multi-agent system where specialized AI agents collaborate on complex tasks with shared memory and tool use.
LLM Inference Pipeline
AI Infrastructure
Production LLM serving on Azure with AKS GPU workers, request batching, KV cache management, and streaming token delivery.
Model Serving Platform
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