For MLOps & ML engineers
A daily KS test on a healthy feature will flag eventually — that's not drift, that's arithmetic. So the team mutes the channel, and the one real regression slides in behind two weeks of ignored PSI alerts. ValidAnytime replaces the fixed threshold with an anytime-valid detector: check as often as you like, get one alarm per real shift, with a false-alarm budget shared across every model in the fleet.
Free to start · no credit card
The model shipped fine in March. By May, precision on the segment that matters had bled four points. Nobody got paged — the drift was slow, and the daily KS test had fired on noise so long the channel was muted in week two. You found it in the quarterly review, from a business metric, weeks after your labels would have shown it. The action item, again: “tune the thresholds.” You both know that just moves the same problem.
Every standard drift test assumes one look at a fixed sample. Run them on a schedule against an unbounded stream and false alarms are guaranteed on stable data — check a fixed 5%-error test 5 times and the real false-positive rate is already ~23%; at 20 looks it passes 64%. Multiply by every feature and model and it's a daily flood, with no global bound on how much of it is false. Evidently and NannyML are strong tools; this is the gap they leave: the alarm itself has no valid-under-peeking guarantee.
Prediction error, PSI, a calibration score, precision on a segment, or a raw feature statistic — send the number through one HTTP call or the SDK. No raw rows, no model weights, just the value.
Auto-config replays your history and shows whether, and on which day, it would have caught your last drift — and stays quiet on the months that were fine. If a config false-alarms on normal history, it never goes live. This is where you stop trusting our marketing and start trusting your own data.
ARL-calibrated Shiryaev–Roberts (SR) e-detectors accumulate evidence; online FDR (e-LOND) spends a single false-discovery budget across every model and feature. Add monitors without adding noise.
Paste a metric series — accuracy, PSI, latency — and watch an anytime-valid detector show whether, and at which point, it would have caught the regression. Nothing is uploaded; it runs in your browser. Then do it for real on the metric that burned you last quarter.
Model accuracy — bad retrain, slow drift
Synthetic, illustrative — runs in your browser, nothing uploaded.
From the seeded demo — synthetic stream, real engine. Load it in the live dashboard →
Keep them. Evidently's test breadth and reports are excellent for exploration and CI; NannyML's CBPE/DLE label-free performance estimation is a real, distinctive capability we don't try to replicate. ValidAnytime is the trustworthy alarm layer on top — the piece that turns “we have monitors” into “when it fires, we look.” Point your existing metric outputs at our detector and get a valid-under-peeking alarm with a certificate.
Point ValidAnytime at one stream and it replays your own history — free, no credit card.