Real-time analytics is less about a single clever model and more about a pipeline that never stalls. The hard parts live between the boxes on the architecture diagram.
Ingest without backpressure
Events arrive in bursts. We decouple ingestion from processing with a streaming buffer so a spike in traffic never drops data or blocks the producers writing it.
Features, computed once
A shared feature layer computes signals once and serves them to both training and inference. That eliminates training/serving skew — the silent killer of model accuracy in production.
Decisions you can trust
Every prediction is logged with its inputs and model version, so a decision can always be explained and replayed. Monitoring watches for drift and alerts before accuracy quietly degrades.