Incident response for ML systems
Detect, contain and learn from ML failures, from silent quality drops to harmful outputs
A taste of a lesson
Users report our support chatbot is giving wrong refund rules since this morning. We are not sure why. What first?
Contain first, investigate second. Wrong refund rules can cost customers money, so this deserves a high severity. Assign an incident lead, then limit harm: roll back to yesterday's configuration or model if something changed, or switch refund questions to a fixed answer or a human handoff. Tell support staff now so they can correct customers. Then investigate what changed this morning: prompts, knowledge base content, model version, retrieval settings. Record a timeline as you go. Quick check: which of those changes could you reverse in under ten minutes today, and who has permission to do it?
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
- Recognise ML incidents beyond outages, including silent quality drops and bias
- Define severity levels based on impact on people
- Contain incidents with rollback, kill switches and safe fallbacks
- Communicate clearly with stakeholders during an incident
- Run a blameless postmortem with follow up actions that get done
Lesson plan
- 1 What an ML incident looks like Recognise the range of ML incidents and how they are usually detected. Start
- 2 Severity and ownership Define severity levels by impact and decide who leads a response. Start
- 3 Contain first Limit harm quickly with rollback, kill switches and fallbacks. Start
- 4 Runbooks Write a runbook for one ML system so anyone on call can act. Start
- 5 Communicating during incidents Keep stakeholders and affected users informed honestly and on a rhythm. Start
- 6 Blameless postmortems Learn from incidents and turn lessons into completed actions. Start
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About this tutor
For anyone who runs or depends on a machine learning system: engineers, product owners, support leads and managers. You will learn what counts as an ML incident, which is wider than an outage: silent quality drops, harmful or offensive outputs, broken data pipelines, and bias discovered after launch. Then you build the response: detection, severity levels, runbooks, rollback and kill switches, safe fallback behaviour, and clear communication with users and stakeholders. The course ends with blameless postmortems and follow up actions that actually get done. Lessons use realistic scenarios that you work through step by step, and suit both technical and non technical roles.
Reviews
4.7
3 ratingsSample
- Tariq S.Sample
Our postmortems used to end with vague actions. Owners and dates, reviewed later, made a real difference.
- Henrik J.Sample
The scenarios were realistic. I would have liked one more on bias discovered after launch, but the containment habit is what I needed most.
- Gabriela V.Sample
I am a support lead, not an engineer, and this was the first ML course written for people like me too. Our team now has a tested handoff switch.
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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