Master AI infrastructure design, observability, and API management through specialized tool interfaces.
Specialized interfaces: Network Designer, Log Console, and API Gateway.
Architecture configuration complete.
Monitoring configuration saved.
Gateway configuration complete.
You are a CloudScale AI infrastructure engineer designing a production ML inference pipeline. Configure all infrastructure components for high availability, low latency, and scalability.
Complete the ML inference architecture by placing these 4 components in the correct positions. The diagram shows data flow from Clients → Load Balancer → Model Servers → GPU Cluster.
Architecture Overview:
A production ML pipeline needs: (1) an entry point for external traffic, (2) response caching to reduce latency, (3) a vector store for embeddings/RAG, and (4) observability for the entire system. Place these supporting components around the core inference path.
API Gateway
Entry point for all client requests. Sits at the top-left as the first component clients interact with before reaching the Load Balancer. Handles authentication, rate limiting, and request routing.
Cache Layer
Positioned at the top near the entry point to intercept requests early. Caches embeddings and frequent responses to reduce GPU load and latency. Cache hits bypass the expensive inference path.
Vector DB
Placed at the bottom near GPU Cluster. Stores embeddings for RAG (Retrieval-Augmented Generation). Model servers query Vector DB for context before generating responses on GPUs.
Monitoring
Observes the entire pipeline from the side. Collects metrics from all components (GPU utilization, latency, error rates). Essential for detecting OOM issues, latency spikes, and triggering auto-scaling.
Click "Validate Diagram" to verify your placements are correct before configuring the settings below.
Algorithm: Use "least-conn" for ML workloads (balances by active connections)Health Check: Use "10s" for responsive failure detectionReplicas: Use 3+ for high availabilityAuto-scaling: Use "latency" or "queue" for ML workloadsGPU Type: A100 for high throughput, T4 for cost efficiencyGPU Count: 4+ GPUs for production inferenceCache Type: Redis for distributed cachingTTL: 5 minutes for embedding caches, 1 hour for static responsesDatabase: Pinecone or Weaviate for managed, Milvus for self-hostedIndex Type: HNSW for best accuracy/speed tradeoffMetrics: Prometheus for open-source, Datadog for managedAlerting: PagerDuty for production incidentsYou are a ModelOps engineer investigating a production incident. Analyze the logs, identify the root cause, configure alerting thresholds, and set up monitoring dashboards.
Error Threshold: 1% for production SLAAlert Severity: Critical for high-impact issuesRetention: 30+ days for incident analysisAggregation Window: 5 minutes for alert smoothingLatency SLO: P99 < 200ms for inference APIsMemory exhaustionRemediation: Increase memory or scale horizontallyDashboard: Grafana for metrics visualizationNotification: PagerDuty or Slack for alertsSampling: 100% for critical production logsYou are launching the AIaaS Platform inference API to enterprise customers. Configure the API Gateway with rate limiting, authentication, caching, and resilience patterns for production readiness.
Requests/min: 100-500 for standard tier, 1000 for enterpriseBurst Limit: 20-50 to handle traffic spikesAuthentication: JWT or OAuth2 for enterprise, API Key for simple useAPI Version: v1 (stable) for productionResponse Caching: 5 minutes for GET endpointsTimeout: 30-60 seconds for inference APIsCircuit Breaker: 10 failures to triggerRecovery Time: 60 seconds before retryRetry Policy: 3 retries for transient failuresCORS: Specific Origins for web clientsValidation: Full validation for productionLogging: Info level for production