Lion Cipher Lab
Monitor an ML model for data drift with Evidently
- AI Security
- Beginner
- ~40 min
- Free · no account required
Why it matters
A deployed model does not fail loudly. It keeps returning predictions with total confidence while the world it was trained on drifts out from under it -- new customer behavior, a changed upstream feed, a different season -- and its accuracy erodes with no error message to warn you. The people who run AI responsibly are the ones who watch the inputs, not just the demo, and catch that erosion before it costs something. That watching is monitoring, and drift detection is its most useful first move: it turns "the model feels off lately" into a specific, dated, reproducible signal about exactly…
What you'll learn
- ML model monitoring
- data drift detection
- Evidently
- reference vs current datasets
- reading drift reports
- when NOT to retrain
Steps in this lab
- Why a working model quietly goes bad
- Data drift vs. concept drift, named plainly
- Meet Evidently, and install it
- Split your OWN data into reference and current
- Generate a data-drift report
- Read the drift signals honestly
- The honest caveat: drift is a signal, not a verdict
- What you can now do that most people can't