Monitoring models and catching drift
Notice when a live model starts getting worse, even before the true answers arrive
A taste of a lesson
Our drift dashboard shows PSI of 0.4 on the 'income' feature overnight. Should we retrain immediately?
Not yet. A large overnight jump is more often a pipeline change than a real shift in your customers. Check first: did the income field change units, currency or source? Are many values suddenly missing or filled with a default like zero? Compare a few raw records from before and after. If the data is genuinely different, look at prediction distributions and any early quality signals before retraining, since retraining on broken data makes things worse. Quick exercise: list three upstream changes that could produce this jump and how you would confirm or rule out each one.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Separate operational monitoring from model quality monitoring
- Choose quality proxies when true labels arrive late or never
- Distinguish data drift, concept drift, label shift and pipeline bugs
- Apply drift statistics such as PSI with their caveats
- Design alerts and retraining triggers tied to clear actions
Lesson plan
- 1 Two layers of monitoring Set up operational and quality monitoring and know what each can catch. Start
- 2 When labels are late or missing Pick proxies and sampling plans that give early warning without true outcomes. Start
- 3 Kinds of drift and look alikes Tell real drift apart from pipeline bugs and identify its type. Start
- 4 Drift statistics with caveats Use PSI and distribution tests sensibly and interpret them with judgment. Start
- 5 Segments, alerts and LLM outputs Slice metrics, design alerts people act on and monitor generated text. Start
- 6 When and how to retrain Decide retraining triggers and switch models safely. Start
Try asking
About this tutor
For practitioners with a model in production who want to know when it degrades, not weeks later from a complaint. You will separate operational monitoring from model quality monitoring, handle the common case where true labels arrive late or never, and learn the kinds of drift: changes in inputs, in the relationship between inputs and outcomes, and in how often each outcome occurs. Then you choose drift statistics and thresholds with their caveats, slice metrics by segment, design alerts people will not ignore, monitor language model outputs with sampled review, and decide when to retrain. Some familiarity with basic statistics and with deploying a model is assumed.
Reviews
4.7
3 ratingsSample
- Lucy M.Sample
We cut our alerts from fourteen to four, each with an owner. People actually respond now.
- Nadine F.Sample
The 'sudden drift is usually a bug' lesson saved us a pointless retrain. It was a currency field switching source.
- Kaveh A.Sample
Really good on delayed labels and proxies. I wanted more worked examples on LLM monitoring but the core ideas are solid.
About the teacher
MLOps without the ceremony: tracking, versioning, monitoring and responsible deployment
9 tutors 322 lessons taught Sample
I teach the habits that keep machine learning systems trustworthy after the notebook: tracking experiments, versioning data and models, testing, monitoring, handling incidents and documenting models honestly. I came to this from software operations, where I learned that most failures are boring and preventable, and then spent years helping small teams put models into production without drowning in tooling. I...
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