Demo proposal: Green health check, wrong model

[tomasmbrito]

Assignment Proposal

Title

Green health check, wrong model

Names and KTH ID

• Tomás Brito (tmldjb@kth.se)
• Henrique Cavaco (hjcr@kth.se)

Deadline

Week 4

Category

Demo

Description

A CD pipeline decides whether a new version can go live by asking whether it is healthy: does it answer, is it fast, is the response the right shape. That works for ordinary software, because ordinary software fails loudly. A model does not. It keeps answering in milliseconds, with a 200 and the right shape, and it is wrong.

Our example is a small web service that reads a customer review and says whether it is positive or negative. Two versions of it run side by side: the one currently in production, and a candidate waiting to be promoted. The candidate differs by one line of deployment config, which stops the review being lower-cased before the model sees it. The model was trained on lower-cased text, so a review written in capitals arrives as words it has never seen, and it starts guessing.

What we will show, in this order:

• The app in the browser. The same review in lower case and then in capitals gets two different answers. Everyone sees the failure.
• The pipeline on that same commit: build, five unit tests, shadow deploy and health check, all green. Everything a normal CD pipeline knows how to ask says this is ready to ship.
• A behavioural gate that replays a labelled set of reviews through both versions, sees accuracy fall from 0.96 to 0.75, and blocks the promotion before any real user reaches the candidate.
• We find the line, fix it live, push, and watch the pipeline promote.

We finish with where this runs out: the labelled set goes stale, the threshold is a judgement call rather than something you can derive, shadowing doubles the compute, and requests with side effects cannot be mirrored at all.

Relevance

Progressive delivery and quality gates are standard CD practice, and here we apply them to a component that is not deterministic. The failure we use, training-serving skew, is a common way models break in production. The point of the demo is that availability and correctness are different questions, and the usual health check treats them as one.

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