Machine learning systems differ from traditional software-based systems in that the behavior of ML systems is not specified directly in code but is learned from data. Therefore, while traditional software can rely on unit tests and integration tests of the code, here we attempt to add a sufficient set of tests of the data.
1. Test that the distributions of each feature match your expectations. One example might be to test that Feature A takes on values 1 to 5, or that the two most common values of Feature B are "Harry" and "Potter" and they account for 10% of all values. This test can fail due to real external changes, which may require changes in your model.
2. Test the relationship between each feature and the target, and the pairwise correlations between individual signals. It is important to have a thorough understanding of the individual features used in a given model; this is a minimal set of tests, more exploration may be needed to develop a full understanding. These tests may be run by computing correlation coefficients, by training models with one or two features, or by training a set of models that each have one of k features individually removed.
3. Test the cost of each feature. The costs of a feature may include added inference latency and RAM usage, more upstream data dependencies, and additional expected instability incurred by relying on that feature. Consider whether this cost is worth paying when traded off against the provided improvement in model quality.
4. Test that a model does not contain any features that have been manually determined as unsuitable for use. A feature might be unsuitable when it’s been discovered to be unreliable, overly expensive, etc. Tests are needed to ensure that such features are not accidentally included (e.g. via copy-paste) into new models.
5. Test that your system maintains privacy controls across its entire data pipeline. While strict access control is typically maintained on raw data, ML systems often export and transform that data during training. Test to ensure that access control is appropriately restricted across the entire pipeline. Test the calendar time needed to develop and add a new feature to the production model. The faster a team can go from a feature idea to it running in production, the faster it can both improve the system and respond to external changes.
6. Test all code that creates input features, both in training and serving. It can be tempting to believe feature creation code is simple enough to not need unit tests, but this code is crucial for correct behavior and so its continued quality is vital.
* From "What’s your ML Test Score? A rubric for ML production systems" NIPS, 2016
Engineering checklist:
- Test that the distributions of each feature match your expectations
- Test the relationship between each feature and the target
- Test the cost of each feature
- Test that a model does not contain any unsuitable for use feature
- Test that your system maintains privacy controls across its entire data pipeline
- Test all code that creates input features
1. Test that the distributions of each feature match your expectations. One example might be to test that Feature A takes on values 1 to 5, or that the two most common values of Feature B are "Harry" and "Potter" and they account for 10% of all values. This test can fail due to real external changes, which may require changes in your model.
2. Test the relationship between each feature and the target, and the pairwise correlations between individual signals. It is important to have a thorough understanding of the individual features used in a given model; this is a minimal set of tests, more exploration may be needed to develop a full understanding. These tests may be run by computing correlation coefficients, by training models with one or two features, or by training a set of models that each have one of k features individually removed.
3. Test the cost of each feature. The costs of a feature may include added inference latency and RAM usage, more upstream data dependencies, and additional expected instability incurred by relying on that feature. Consider whether this cost is worth paying when traded off against the provided improvement in model quality.
4. Test that a model does not contain any features that have been manually determined as unsuitable for use. A feature might be unsuitable when it’s been discovered to be unreliable, overly expensive, etc. Tests are needed to ensure that such features are not accidentally included (e.g. via copy-paste) into new models.
5. Test that your system maintains privacy controls across its entire data pipeline. While strict access control is typically maintained on raw data, ML systems often export and transform that data during training. Test to ensure that access control is appropriately restricted across the entire pipeline. Test the calendar time needed to develop and add a new feature to the production model. The faster a team can go from a feature idea to it running in production, the faster it can both improve the system and respond to external changes.
6. Test all code that creates input features, both in training and serving. It can be tempting to believe feature creation code is simple enough to not need unit tests, but this code is crucial for correct behavior and so its continued quality is vital.
* From "What’s your ML Test Score? A rubric for ML production systems" NIPS, 2016
