Machine learning in production: application fields and free-access data records

It is often due to a lack of experience in dealing with machine learning itself, when machine learning projects cannot be successfully completed even with sufficient amounts of data. Missing or unstructured data make it difficult to train models and thus slow down the gain of experience in dealing with machine learning algorithms.

Here, freely available data files can help to gather initial experience and test own machine learning approaches. At present there are not yet many such publicly accessible data sets in the area of production, often due to existing confidentiality obligations, which are also stored on different platforms such as kaggle, ucirvine or openml.

The Fraunhofer IPT has therefore compiled the publicly available data records collected from production in a clearly arranged table on the basis of extensive investigations: 38 data records are currently available in this way. Since the list is maintained and completed by the Aachen scientists, there will be an increasing number soon. The data sets are assigned to seven application areas, which were also worked out at the Fraunhofer IPT.

The first column names the name of the record. The following columns contain the information when the data set was created or last updated and which learning task (e.g. C = Classification or R = Regression) can be expected for the given application field. It also provides information about the number of instances and attributes of the data set. A record that consists of several files is marked accordingly.

When using machine learning in production, it is important to have a sufficient quantity of the parameter to be determined present in the historical data, e.g. the amount of defective parts if rejects are to be predicted. A trained model is then able to reliably predict the conditions it has learned from the historical data. Since an underrepresented class, such as the number of product defects, must occur in sufficient numbers, an additional column with this information has been added to the table.