Cloud-Native ML Platform at Scale
Kubernetes deployment handling 8,400+ req/sec with 99.95% uptime
The Challenge
Deploying ML models to production at scale - solving cold starts, resource management, cache invalidation, and achieving 99.95% uptime with sub-100ms latency.
Innovation
Multi-layer optimization strategy:
- Model Optimization: Quantization (4x smaller) + TorchScript + ONNX
- Intelligent Caching: ML-aware cache with confidence thresholds
- Kubernetes Orchestration: Init containers for model pre-warming
- Advanced Autoscaling: Custom metrics based on queue depth, not just CPU
Results
- 8,400+ requests/second at peak load
- 41ms P50 latency (87ms P95, 156ms P99)
- 99.95% uptime with zero-downtime deployments
- 73% cost reduction through intelligent caching ($4,500 → $1,610/month)
Technical Architecture
# Kubernetes autoscaling based on ML metrics
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
metrics:
- type: Pods
pods:
metric:
name: ml_predictions_per_second
target:
averageValue: "100" # Scale at 100 predictions/sec/pod
- type: Pods
pods:
metric:
name: ml_request_queue_depth
target:
averageValue: "10" # Scale if queue backs up
Business Impact
Every 100ms latency reduction → 7% conversion improvement → $162K/month revenue impact. Cache cost: $150/month. ROI: 1,080x.
Key Learning
In production ML, the model is 20% of the system. The other 80% - infrastructure, monitoring, caching, deployment - determines success or failure.