Master the collocations for shipping AI features, evaluating accuracy, collecting training data, and monitoring model drift.
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1 / 5
The product team worked with ML engineers to ___ AI features that would surface personalised recommendations in the app.
Ship AI features is the standard product and ML engineering collocation for delivering AI-powered functionality to users. 'Release along' is informal. 'Launch around' is imprecise. 'Deploy out' is not standard as a collocation for shipping product features.
2 / 5
Before going to production, the team ran a red-teaming exercise to ___ accuracy of the classification model on real-world inputs.
Evaluate accuracy is the standard ML product collocation for assessing how well a model performs against ground truth labels. 'Check along' and 'test around' are informal. 'Measure out' is too generic for a formal evaluation process.
3 / 5
The data team set up a labelling pipeline to ___ training data at scale using human annotators.
Collect training data is the standard ML collocation for acquiring and curating the labelled examples used to train a model. 'Gather along' and 'create out' are informal. 'Build around' does not convey the data acquisition process.
4 / 5
The ML platform team set up automated alerts to ___ drift in model predictions over time.
Monitor drift is the standard MLOps collocation for detecting degradation in model performance caused by changes in real-world data distributions. 'Track along' and 'watch around' are informal. 'Detect out' is redundant.
5 / 5
After the model update, the team reviewed prompt logs to ___ outputs and check for harmful or inaccurate responses.
Audit outputs is the standard AI product quality collocation for systematically reviewing model responses to identify issues. 'Check along' and 'review around' are informal. 'Inspect out' is not a standard phrase in an AI context.