Your code tests pass and the new model scores better, but an important customer scenario has got worse. Testing an ML system means checking several different promises: that the code runs correctly, that the evaluation resembles future use, and that the model behaves acceptably in the situations you support. Start by taking an inventory of those promises, then define the model's operating conditions. Make the production code testable with small inputs before choosing representative evaluation data. Scenario-level comparisons expose regressions hidden by averages, and inspecting errors gives you new cases to preserve. Finish by testing the surrounding system under controlled failures. The result should be a set of specific expectations you can check again, rather than another reassuring score that nobody knows how to challenge.
Why first: Payne turns production readiness into an inventory of missing checks. His LinkedIn account treats the rubric as a discussion aid, not a mandatory scorecard. Start with the gaps and simple invariants before deciding which more demanding model tests your system needs.
Why second: Penkov defines the missing promise behind many test suites: where the model is expected to work. Brightness, position and other allowed variations become properties to test together. This gives the inventory a model-behavior target instead of assuming that passing code checks proves the model is useful.
Why here: Sragner makes those checks practical to run. The same business code should work locally and in production, with external services replaceable in tests. That keeps the test from exercising a convenient rewrite while the deployed implementation remains coupled to a database or unavailable hardware.
Why here: Small data can reveal broken code without producing a useful model. Sragner and Blazina distinguish that fast check from statistical evaluation, and favor reference outputs for transformations over copying the calculation into its test. Once the implementation is testable, choose assertions that can actually disagree with it.
Why here: Ma questions the evaluation data itself. A correct transformation can still feed a misleading experiment when target timing or a random split leaks future information. His time-based examples establish what the test set must represent before the next two talks examine performance within it.
Why here: Elgendy's detector example shows why a representative test set still needs scenario-level pass criteria. The higher aggregate score hid an important regression. Versioned cases and their result history preserve the product knowledge that would disappear if you kept only the winning model's average.
Why here: Cabrera demonstrates how to discover cases you have not yet named, using audio inputs, model outputs and intersecting metadata filters. His 2023 demonstration makes reusable evaluation slices concrete; turning discovered patterns into regression tests is the next step he proposes, rather than an automatic guarantee of the tool.
Why last: The model can pass every scenario while the service fails around it. Wilms tests explicit expectations under delayed data, unavailable dependencies and other controlled faults. Adding those experiments in a safe environment completes the move from testing predictions to testing the system that delivers them.