Learn More: Machine Learning & Data Science

Data science is the broader discipline of extracting insight from data, covering collection, cleaning, statistical analysis, visualization, and communication of results. Machine learning is one set of techniques within it, specifically the methods that let a model learn patterns from data and make predictions on inputs it has not seen. In practice most real projects are mostly data science: the majority of the effort goes into understanding the problem, gathering usable data, and validating results, while model training is often the shortest phase.

The most common applications are anomaly detection, predictive maintenance, and reducing test time. Anomaly detection flags units whose measured signature deviates from known-good behavior even when every individual measurement passes its limits, which catches failure modes fixed thresholds miss. Predictive maintenance uses trends in vibration, temperature, or current draw to forecast equipment degradation before it causes downtime. Test time reduction uses models to identify which measurements actually predict a failure, so a long test sequence can sometimes be shortened without losing coverage. All three depend on having historical measurement data that was captured and stored properly, which is usually the real constraint.

There is no universal number, and the honest answer is that data quality and labeling matter more than volume. A few thousand well-labeled examples that genuinely represent the failure modes you care about will outperform millions of unlabeled or poorly captured records. The most common blocker we see is not too little data but data that was never stored with the context needed to interpret it: no timestamps aligned across sensors, no record of test conditions, no linkage between a measurement and the eventual outcome for that unit. If you are considering machine learning in the future, the highest-value thing you can do today is fix how measurement data is captured and stored.

Yes, with a distinction between training and inference. Training is computationally heavy and normally happens offline on a workstation or in the cloud, using historical data. Inference, which is running a trained model against new measurements, is comparatively light and can run inside a test application, on a host PC alongside LabVIEW, or in some cases on the real-time controller itself. A common and practical architecture keeps acquisition and deterministic control in LabVIEW on NI hardware, exports measurement data to a database, trains models in Python offline, and then deploys the trained model back into the test application for per-unit scoring.

By evaluating it on data it never saw during training, and by choosing a metric that matches the cost of being wrong. Raw accuracy is misleading whenever classes are imbalanced, which they almost always are in manufacturing, since most units pass. A model that labels everything as good will score highly on accuracy while catching zero defects. Precision, recall, and the confusion matrix tell you what is actually happening, and the right trade-off depends on which error is more expensive for you: shipping a bad unit, or scrapping a good one. That decision is a business judgment, not a modeling one, and it should be made before the model is built.