How do you monitor feature / prediction freshness?

medium

Answer

$Freshness = time between event and its reflection in served feature.$
$\operatorname{max}(\mathrm{current}_{\mathrm{time}}\; - \;\mathrm{feature}\mathrm{updated}_{\mathrm{at}})$
$(2) SLA per feature (real-time features: seconds; daily features: hours).$
$(3) Alert when lag exceeds SLA.$
$(4) Root causes: streaming pipeline lag, batch job failed, source system down.$
$Tools: Kafka lag metrics (consumer group), Airflow SLA, feature-store metadata.$
$Critical for time-sensitive predictions (fraud, recsys).$

Check yourself — multiple choice

Random
Freshness = event-to-served-feature lag; monitor $\operatorname{max}(\mathrm{now}\; - \;\mathrm{feature}\mathrm{updated}_{\mathrm{at}})$ per SLA; alert on breach; root cause streaming lag / batch failed / source down; Kafka lag / Airflow SLA / FS metadata
Not important
Just count

Freshness: event-to-serve lag; alert per feature SLA.

#monitoring#features

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