An ML system often relies on a complex pipeline rather than a single running binary.
1. Test the reproducibility of training. Train two models on the same data, and observe any differences in aggregate metrics, sliced metrics, or example-by-example predictions. Large differences due to non-determinism can exacerbate debugging and troubleshooting.
2. Unit test model specification code. Although model specifications may seem like “configuration”, such files can have bugs and need to be tested. Useful assertions include testing that training results in decreased loss and that a model can restore from a checkpoint after a mid-training job crash.
3. Integration test the full ML pipeline. A good integration test runs all the way from original data sources, through feature creation, to training, and to serving. An integration test should run both continuously as well as with new releases of models or servers, in order to catch problems well before
they reach production.
4. Test model quality before attempting to serve it. Useful tests include testing against data with known correct outputs and validating the aggregate quality, as well as comparing predictions to a previous version of the model.
5. Test that a single example or training batch can be sent to the model, and changes to internal state can be observed from training through to prediction. Observing internal state on small amounts of data is a useful debugging strategy for issues like numerical instability.
6. Test models via a canary process before they enter production serving environments. Modeling code can change more frequently than serving code, so there is a danger that an older serving system will not be able to serve a model trained from newer code. This includes testing that a model can be loaded into the production serving binaries and perform inference on production input data at all. It also includes a canary process, in which a new version is tested on a small trickle of live data.
7. Test how quickly and safely a model can be rolled back to a previous serving version. A model “roll back” procedure is useful in cases where upstream issues might result in unexpected changes to model quality. Being able to quickly revert to a previous known-good state is as crucial with ML models as with any other aspect of a serving system.
* From "What’s your ML Test Score? A rubric for ML production systems" NIPS, 2016
Engineering checklist:
- Test the reproducibility of training
- Unit test model specification code
- Integration test the full ML pipeline
- Test model quality before attempting to serve it
- Test that a single example or training batch can be sent to the model
- Test models via a canary process before they enter production serving environments
- Test how quickly and safely a model can be rolled back to a previous serving version
1. Test the reproducibility of training. Train two models on the same data, and observe any differences in aggregate metrics, sliced metrics, or example-by-example predictions. Large differences due to non-determinism can exacerbate debugging and troubleshooting.
2. Unit test model specification code. Although model specifications may seem like “configuration”, such files can have bugs and need to be tested. Useful assertions include testing that training results in decreased loss and that a model can restore from a checkpoint after a mid-training job crash.
3. Integration test the full ML pipeline. A good integration test runs all the way from original data sources, through feature creation, to training, and to serving. An integration test should run both continuously as well as with new releases of models or servers, in order to catch problems well before
they reach production.
4. Test model quality before attempting to serve it. Useful tests include testing against data with known correct outputs and validating the aggregate quality, as well as comparing predictions to a previous version of the model.
5. Test that a single example or training batch can be sent to the model, and changes to internal state can be observed from training through to prediction. Observing internal state on small amounts of data is a useful debugging strategy for issues like numerical instability.
6. Test models via a canary process before they enter production serving environments. Modeling code can change more frequently than serving code, so there is a danger that an older serving system will not be able to serve a model trained from newer code. This includes testing that a model can be loaded into the production serving binaries and perform inference on production input data at all. It also includes a canary process, in which a new version is tested on a small trickle of live data.
7. Test how quickly and safely a model can be rolled back to a previous serving version. A model “roll back” procedure is useful in cases where upstream issues might result in unexpected changes to model quality. Being able to quickly revert to a previous known-good state is as crucial with ML models as with any other aspect of a serving system.
* From "What’s your ML Test Score? A rubric for ML production systems" NIPS, 2016
