Realtime ML Predictions System Development
Trained ML model is valuable only if predictions available at right moment with minimal latency. Realtime ML predictions system is not just "run model", it's full infrastructure with low-latency serving, quality monitoring and automatic model switching.
Architecture: Market Data → Feature Pipeline → Feature Store (Redis) → ML Model Server (FastAPI) → Prediction Cache (Redis) → Trading/Dashboard/Monitoring.
Feature Pipeline for realtime: circular buffer for storing last N candles. Calculate features on-the-fly for each new candle update. < 10ms total latency.
ML Model Serving with FastAPI: load models on startup, inference endpoint returns predictions with confidence and latency.
Batching for throughput optimization: collect requests and batch process them, reducing overhead.
Model Registry and Versioning: MLflow for model storage with versions, training parameters, metrics.
Production quality monitoring:
- Directional accuracy: predictions correct direction?
- High confidence accuracy: high confidence predictions more accurate?
- Recent accuracy trend: model degradation detection
Latency monitoring: P50, P95, P99 latency via Prometheus + Grafana. SLA: P95 < 50ms, P99 < 100ms.
Automatic rollback: if quality degrades below threshold, automatically rollback to previous model version.
Develop production-ready ML serving system: FastAPI inference server, batching for throughput, MLflow model registry, realtime quality monitoring and automatic rollback on degradation.